This file contains the results of a bib search, hand-pruned to eliminate false hits, leaving 287 legitimate hits. The database only goes back to 1986, but my original scale space article appeared in '83, so we're missing a few of years' worth. In any event, the original citation is Andrew Witkin. Scale space filtering. In Proc. International Joint Conference on Artificial Intelligence, Karlsruhe, 1983. ------------------------------------------------------------ Author Fatemi-Ghomi, N.; Palmer, P.L.; Petrou, M.; Dept. of Electron. & Electr. Eng., Surrey Univ., Guildford, UK Title Multiresolution texture segmentation Source IEE Colloquium on 'Texture Classification: Theory and Applications (Digest No.1994/178); Part: London, UK; Part: 7 Oct. 1994; Sponsored by: IEE; IEE Colloquium on 'Texture Classification: Theory and Applications (Digest No.1994/178); London, UK; IEE; 38; 1994; pp. 2/1-3 Abstract One of the major developments in texture segmentation in recent years has been the use of multiresolution descriptions of these images. One may consider this multiresolution description to provide information about the image contained in ever smaller regions of the frequency domain, and thus provides a powerful tool for the discrimination of similar textures. It has recently been established that use of scale-space-filtering by ever wider filters, is equivalent to a decomposition of the image in terms of wavelets. The big advantage of the wavelet description of an image is that it is invertible. In this paper we use successive transformations of textured images in terms of wavelets to produce lower resolution descriptions of the image. This procedure can be thought of as dynamically focussing on the important region of the frequency domain for the particular texture in the image Thesaurus image segmentation; image texture Other Terms texture segmentation; multiresolution descriptions; frequency domain; scale-space-filtering; successive transformations; textured images ClassCodes C1250; C5260B Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4824740 AbstractNos. C9412-1250-334 References 3 Country Pub. UK date 1232 ------------------------------------------------------------ Author Ji Yu Shi; Hung Tat Tsui; Dept. of Electron. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong Title Scale space filtering by Fejer kernel Source Proceedings of ICSIPNN '94. International Conference on Speech, Image Processing and Neural Networks; Part: vol.2; Part: Hong Kong; Part: 13-16 April 1994; Sponsored by: IEEE Signal Process. Soc.; HKIE Electron. Div.; IEE Hong Kong Centre; ISSIPNN '94. 1994 International Symposium on Speech, Image Processing and Neural Networks Proceedings (Cat. No.94TH0638-7); New York, NY, USA; IEEE; 2 vol. (xxxvii+xi+797); 1994; pp. 638-41 vol.2 Abstract A multiscale description of the raw signal data must be first obtained before more abstract representations and descriptions are generated. Many unsuccessful attempts have been made to let this initial description be as compact and complete as possible. We propose a new filtering kernel called Fejer kernel to try to tackle this problem. The behaviors of signals' features in different scales are investigated in Fejer scale space-(x, Delta ) plane. Simulation results are shown to demonstrate this new multiscale description for a signal. It compares favorably with Gaussian scale space filtering in that it brings out the main features of the signal under large scales. Gaussian filtering just makes every feature smoother. Further investigation to refine this new multiscale signal description is under development Thesaurus filtering and prediction theory; signal processing Other Terms Fejer kernel; scale space filtering; signal data; multiscale description; abstract representations; filtering kernel; Fejer scale space; simulation results; Gaussian scale space filtering; multiscale signal description; signal representation ClassCodes B6140; C1260 Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4824557 AbstractNos. B9412-6140-269; C9412-1260-245 ISBN or SBN 0 7803 1865 X References 12 U.S. Copyright Clearance Center Code 0 7803 1865 X/94/$3.00 Country Pub. USA date 1226 ------------------------------------------------------------ Author Wong, Y.-f.; Lawrence Livermore Nat. Lab., Livermore, CA, USA Title A clustering filter for scale-space filtering and image restoration Source Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; Part: New York, NY, USA; Part: 15-17 June 1993; Sponsored by: IEEE Comput. Soc. Tech. Committee on Pattern Anal. & Mach. Intelligence; Proceedings. 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.93CH3309-2); Los Alamitos, CA, USA; IEEE Comput. Soc. Press; xviii+804; 1993; pp. 668-9 Abstract A nonlinear clustering filter is derived using the maximum entropy principle. This filter is governed by a single-scale parameter and uses local characteristics in the data to determine the scale parameter in the output space. It provides a mechanism for removing impulsive noise, preserving edges, and improving smoothing of nonimpulsive noise. It also presents a scheme for nonlinear scale-space filtering. Comparisons with Gaussian scale- space filtering are made using real images. It is demonstrated that the clustering filter gives much better results Thesaurus entropy; filtering and prediction theory; image recognition; image reconstruction; parameter estimation Other Terms impulsive noise removal; edge preservation; nonimpulsive noise smoothing; image restoration; nonlinear clustering filter; maximum entropy principle; single-scale parameter; scale parameter; output space; nonlinear scale-space filtering ClassCodes B6140C; C1250; C1260 Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4823739 AbstractNos. B9412-6140C-424; C9412-1250-282 ISBN or SBN 0 8186 3880 X References 5 U.S. Copyright Clearance Center Code 1063-6919/93/$03.00 Country Pub. USA date 1215 ------------------------------------------------------------ Author Mokhtarian, F.; NTT Basic Res. Lab., Tokyo, Japan Title Multi-scale, torsion-based shape representations for space curves Source Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; Part: New York, NY, USA; Part: 15-17 June 1993; Sponsored by: IEEE Comput. Soc. Tech. Committee on Pattern Anal. & Mach. Intelligence; Proceedings. 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.93CH3309-2); Los Alamitos, CA, USA; IEEE Comput. Soc. Press; xviii+804; 1993; pp. 660-1 Abstract Space curves are efficient, highly descriptive features of 3-D objects. A new multiscale shape representation (the resampled torsion scale space or TSS image) for space curves suitable for recognition of a noisy curve at any scale or orientation is introduced. A two-phase matching algorithm consisting of TSS matching (using extrema of TSS zero-crossing contours) followed by transformation parameter optimization demonstrates the usefulness of the representation in recognition tasks Thesaurus image recognition; image sequences Other Terms noisy curve recognition; zero-crossing contour extrema; torsion- based shape representations; space curves; 3-D objects; multiscale shape representation; resampled torsion scale space; TSS image; two-phase matching algorithm; transformation parameter optimization ClassCodes B6140C; C1250 Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4823735 AbstractNos. B9412-6140C-421; C9412-1250-278 ISBN or SBN 0 8186 3880 X References 2 U.S. Copyright Clearance Center Code 1063-6919/93/$03.00 Country Pub. USA date 1215 ------------------------------------------------------------ Author Lions, P.-L.; CEREMADE, Paris IX Univ., France Title Axiomatic derivation of image processing models Source Mathematical Models & Methods in Applied Sciences; Math. Models Methods Appl. Sci. (Singapore); vol.4, no.4; Aug. 1994; pp. 467-75 Abstract We briefly review the derivation due to Alvarez, Guichard, Morel (1993) and the author of mathematical models in image processing. We deduce from classical axioms in computer vision some nonlinear partial differential equations of evolution type that correspond to general multi-scale analysis (scale-space). We also obtain specific nonlinear models that satisfy additional invariances which are relevant for the analysis of images Thesaurus computer vision; nonlinear differential equations; partial differential equations Other Terms image processing models; computer vision; nonlinear partial differential equations; multi-scale analysis; scale-space ClassCodes C1250; C5260B; C4170 Article Type Theoretical / Mathematical Coden MMMSEU Language English RecordType Journal ControlNo. 4822130 AbstractNos. C9412-1250-230 ISSN 02182025 References 24 Country Pub. Singapore date 1230 ------------------------------------------------------------ Author Theimer, W.M.; Mallot, H.A.; Inst. fur Neuroinf., Ruhr-Univ., Bochum, Germany Title Phase-based binocular vergence control and depth reconstruction using active vision Source CVGIP: Image Understanding; CVGIP, Image Underst. (USA); vol.60, no.3; Nov. 1994; pp. 343-58 Abstract We present a technique for guiding vergence movements for an active stereo camera system and for calculating dense disparity maps. Both processes are described in the same theoretical framework based on phase differences in complex Gabor filter responses, modeling receptive field properties in the visual cortex. While the camera movements are computed with input images of coarse spatial resolution, the disparity map calculation uses a finer resolution in the scale space. The correspondence problem is solved implicitly by restricting the disparity range around zero disparity (Panum's area in the human visual system). The vergence process is interpreted as a mechanism to minimize global disparity, thereby setting a 3D region of interest for subsequent disparity detection. The disparity map represents smaller local disparities as an important cue for depth perception. Experimental data for the integrated performance of vergence in natural scenes followed by disparity map calculations are presented Thesaurus computer vision; stereo image processing; visual perception Other Terms phase-based binocular vergence control; depth reconstruction; active vision; guiding vergence movements; active stereo camera system; dense disparity maps; phase differences; complex Gabor filter responses; receptive field properties; visual cortex; camera movements; input images; spatial resolution; correspondence problem; human visual system; Panum's area; zero disparity; 3D region; depth perception; integrated performance; natural scenes ClassCodes B6140C; C5260B Article Type Theoretical / Mathematical Coden CIUNEJ Language English RecordType Journal ControlNo. 4822092 AbstractNos. B9412-6140C-361; C9412-5260B-232 ISSN 10499660 References 26 U.S. Copyright Clearance Center Code 1049-9660/94/$6.00 Country Pub. USA date 1233 ------------------------------------------------------------ Author Hwang, W.-L.; Mallat, S.; Dept. of Comput. Sci., New York Univ., NY, USA Title Characterization of self-similar multifractals with wavelet maxima Source Applied and Computational Harmonic Analysis; Appl. Comput. Harmon. Anal. (USA); vol.1, no.4; Sept. 1994; pp. 316-28 Abstract Self-similar multifractals have a wavelet transform whose maxima define self-similar curves in the scale-space plane. We introduce an algorithm that recovers the affine self-similarity parameters through a voting procedure in the corresponding parameter space. The voting approach is robust with respect to renormalization noise and can recover the value of parameters having random fluctuations. We describe numerical applications to Cantor measures, dyadic multifractals, and the study of diffusion limited aggregates Thesaurus fractals; wavelet transforms Other Terms self-similar multifractals; wavelet maxima; wavelet transform; self-similar curves; scale-space plane; affine self-similarity parameters; voting procedure; parameter space; renormalization noise; random fluctuations; Cantor measures; dyadic multifractals; diffusion limited aggregates ClassCodes B0230; B0290Z; C1130; C4190 Article Type Theoretical / Mathematical Coden ACOHE9 Language English RecordType Journal ControlNo. 4812736 AbstractNos. B9412-0230-002; C9412-1130-001 ISSN 10635203 References 13 U.S. Copyright Clearance Center Code 1063-5203/94/$6.00 Country Pub. USA date 1231 ------------------------------------------------------------ Author Jang, B.K.; Health Sci. Res. Lab., Eastman Kodak Co., Rochester, NY, USA Title Gaussian and morphological scale space for shape analysis of medical images Source 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference; Part: vol.2; Part: San Francisco, CA, USA; P art: 31 Oct.-6 Nov. 1993; 1993 IEEE Conference Record. Nuclear Science Symposium and Medical Imaging Conference (Cat. No.93CH3374-6); New York, NY, USA; IEEE; 3 vol. 1930; 1993; pp. 1327-31 vol.2 Editor Klaisner, L. Abstract The construction of scale space requires the smoothing of the given image to generate a set of corresponding images at other coarser scales, and the extraction of features at these scales. Various methods of smoothing, combined with various feature extractors, will result in drastically different scale space representations. The particular application and desired criteria determine the choice of smoothing and feature extractor. Here the author first puts forward a framework for the scale space representation. Then he focuses on the Gaussian and morphological scale space for planar shape analysis of medical images. The discussion of the various scale space methods is organized into three categories - boundary approach, region approach, and hybrid approach. Properties, limitations, performance, and applications of these scale space methods are discussed Thesaurus feature extraction; image segmentation; medical image processing Other Terms morphological scale space; medical images shape analysis; Gaussian space; planar shape analysis; boundary approach; region approach; hybrid approach; feature extractor; smoothing ClassCodes A8710; A8760; A8770E Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4811940 AbstractNos. A9424-8710-004 ISBN or SBN 0 7803 1487 5 References 13 U.S. Copyright Clearance Center Code 0 7803 1487 5/94/$04.00 Country Pub. USA date 1219 ------------------------------------------------------------ Author Topkar, V.; Sood, A.K.; Kjell, B.; Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA Title Performance analysis of 1-D scale-space algorithms for pulse detection in noisy image scans Source CVGIP: Image Understanding; CVGIP, Image Underst. (USA); vol.60, no.2; Sept. 1994; pp. 191-209 Abstract Scale-space representation is a topic of active research in computer vision. The focus of the research so far has been on coarse-to-line focusing methods, image reconstruction, and computational aspects. However, not much work has been done on the signal detection problem, i.e., detecting the presence or absence of signal models from noisy image scans using the scale- space. In this paper we propose four 1-D signal detection algorithms for separating pulse signals in an image scan from the background in the scale-space domain. These algorithms do not need any thresholding to detect the zero-crossings (zc's) at any of the scales Thesaurus computer vision; image reconstruction; noise; signal detection Other Terms 1-D scale-space algorithms; pulse detection; noisy image scans; computer vision; image reconstruction; signal detection; zero- crossings; image scan ClassCodes B6140C; C5260B; C1250 Article Type Practical Coden CIUNEJ Language English RecordType Journal ControlNo. 4803851 AbstractNos. B9412-6140C-107; C9412-5260B-064 ISSN 10499660 References 17 U.S. Copyright Clearance Center Code 1049-9660/94/$6.00 Country Pub. USA date 1231 ------------------------------------------------------------ Author Kelch, J.; Wein, B.; Rogowski-Inst. for Electr. Eng., Tech. Hochschule Aachen, Germany Title Segmentation of the tongue surface in ultrasonic images using modified scale space filtering Source Proceedings of IEEE Ultrasonics Symposium; Part: vol.2; Part: Baltimore, MD, USA; Part: 31 Oct.-3 Nov. 1993; Sponsored by: Ultrasonics Ferroelectric & Frequency Control Soc; IEEE 1993 Ultrasonics Symposium Proceedings (Cat. No.93CH3301-9); New York, NY, USA; IEEE; 2 vol. 1270; 1993; pp. 947-50 vol.2 Editor Levy, M.; McAvoy, B.R. Abstract For the detection of motility disturbances during swallowing and articulation B-mode sonography is used. In general these images of a median sagittal plane through the tongue and the floor of the mouth are manually contoured by the physician, because it is quite difficult to get subtle information concerning tongue movement and motility from the real-time video images. The digitised images built a spatiotemporal space with a depth of about 70 frames. The authors' application demonstrates a way to extract the tongue contour automatically. They use a modified scale space filter for edge detection. In this way contour segments of the tongue dorsum and other object parts are extracted. A model of the tongue supports identification of the tongue segments and interpolation of the plane in the spatiotemporal space. A virtual three-dimensional view of these contours of the spatiotemporal space assists the physician to a diagnosis of the movement disturbance Thesaurus acoustic signal processing; biomedical ultrasonics; edge detection; filtering and prediction theory; image segmentation; medical image processing; patient diagnosis Other Terms tongue surface; ultrasonic images; modified scale space filtering; motility disturbances; swallowing; articulation; B- mode sonography; median sagittal plane; mouth; tongue movement; spatiotemporal space; digitised images; tongue contour; edge detection; virtual three-dimensional view; diagnosis ClassCodes A8760B; A8770E; A4360; B7510B; B7820; B6140C; C7330; C1250; C1260; C5260B Article Type Practical Language English RecordType Conference ControlNo. 4800558 AbstractNos. A9423-8760B-012; B9412-7510B-011; C9412-7330-020 ISBN or SBN 0 7803 1278 3 References 7 U.S. Copyright Clearance Center Code 1051-0117/93/$4.00 Country Pub. USA date 1219 ------------------------------------------------------------ ------------------------------------------------------------ Author Lefevre, M.; Preteux, F.; Lavayssiere, B.; Dpt. Image, ENST, Paris, France Title Radiographic inspection, defects segmentation and multiscale analysis Source Image Algebra and Morphological Image Processing V; Part: San Diego, CA, USA; Part: 25-26 July 1994; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2300; 1994; pp. 122-32 Abstract In radiographic inspection used by Electricite De France (EDF) for pipe control in nuclear power plants, pipe radiographs are digitized according to a well-defined protocol. The aim of EDF consists of developing a non-destructive testing system for identifying defects. Because of the poor quality of the digitized images, the segmentation problem is very difficult to solve. In this paper, we propose an approach based on a multiscale analysis to simultaneously achieve defect-enhancement and noise-filtering and perform accurate defect segmentation using mathematical morphology. The principle consists of defining a one-parameter family of operators which are successively applied to the digitized images after drift-removal. Equivalently, the multiscale analysis can be expressed as the solution of an evolution equation, whose analytical form is conditioned by the specific properties of the operators (e.g. mean curvature motion, affine scale-space). This results in a family of images with increasing smoothness and consistent edge preservation. Within this framework, we propose a global critical scale estimation via a multiscale analysis of second order local moments of the image. This value minimizes noise ratio without altering edges. Following the filtering step, the final segmentation is easily performed via a morphological approach based on the connection cost concept. The developed method has been tested on a set of radiographs including various defect patterns, leading to impressive segmentation results. Robustness with respect to noise ratio and optimal scale are discussed Thesaurus edge detection; flaw detection; image segmentation; mathematical morphology; noise; parameter estimation; radiography Other Terms radiographic inspection; defects segmentation; multiscale analysis; Electricite De France; pipe control; nuclear power plants; EDF; non-destructive testing system; digitized images; edge fuzziness; defect-enhancement; noise-filtering; mathematical morphology; drift-removal; evolution equation; analytical form; mean curvature motion; affine scale-space; consistent edge preservation; global critical scale estimation; second order local moments; final segmentation ClassCodes B6140C; B0590; C1250; C5260B Article Type Theoretical / Mathematical Coden PSISDG Language English RecordType Conference ControlNo. 4791799 AbstractNos. B9411-6140C-528; C9411-1250-357 ISSN 0277786X References 25 U.S. Copyright Clearance Center Code 0 8194 1624 X/94/$6.00 Country Pub. USA date 1229 ------------------------------------------------------------ Author Peters, R.A., II; Nichols, J.A.; Dept. of Electr. Eng., Vanderbilt Univ., Nashville, TN, USA Title Properties of image sequences generated through opening residuals Source Image Algebra and Morphological Image Processing V; Part: San Diego, CA, USA; Part: 25-26 July 1994; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2300; 1994; pp. 57-68 Abstract A type of Euclidean granulometry is the sequence of images created through the opening of a single image with a family of mutually open structuring elements. The sequence is a morphological analogue of linear scale space. In such a granulometry, the minimum size of features increases in successive images, whereas in scale space the maximum spatial frequency decreases, and the images become increasingly lowpass. The opening spectrum is the image sequence created by computing the differences between successive images in a Euclidean granulometry. Whereas the difference sequence from scale space has a Laplacian bandpass characteristic, the opening spectrum does not have analogous size band-limited properties. Moreover, the images in an opening spectrum are usually not open. One can create a size band-limited sequence analogous to a Laplacian sequence through recursive opening of the tophat - the residual difference between the image and its opening. The recursion is to open the tophat with a smaller structuring element then to subtract that opening from the tophat. We call the result the tophat spectrum. This paper states and proves a number of properties of the tophat and the tophat spectrum. These include: the tophat is antiextensive and idempotent (but not increasing); each image in the tophat spectrum is size-limited and open; the structuring element family need not be mutually open to generate a tophat spectrum; if the SE family is mutually open, and the original image is binary, each image in the tophat spectrum includes the open part of the corresponding image from the opening spectrum; and, the tophat spectrum is identical to the opening spectrum created with a family of flat, one-dimensional structuring elements Thesaurus image sequences; mathematical morphology; transforms Other Terms image sequences; opening residuals; Euclidean granulometry; single image; open structuring elements; morphological analogue; linear scale space; feature size; scale space; maximum spatial frequency; opening spectrum; successive images; difference sequence; tophat; size band-limited sequence; recursive opening; antiextensive; residual difference; tophat spectrum; idempotent; size-limited image; flat one-dimensional structuring elements ClassCodes B6140C; C1250; C5260B Article Type Theoretical / Mathematical Coden PSISDG Language English RecordType Conference ControlNo. 4791793 AbstractNos. B9411-6140C-522; C9411-1250-352 ISSN 0277786X References 17 U.S. Copyright Clearance Center Code 0 8194 1624 X/94/$6.00 Country Pub. USA date 1229 ------------------------------------------------------------ Author Bijaoui, A.; Obs. de la Cote d'Azur, Nice, France Title Partial reconstruction and astronomical image inventory by the wavelet transform Source Proceedings of 7th International Conference on Image Analysis and Processing; Part: Monopoli, Italy; Part: 20-22 Sept. 1993; Proceedings of the 7th International Conference on Image Analysis and Processing. Progress in Image Analysis and Processing III; Singapore; World Scientific; xxiv+733; 1994; pp. 411-16 Editor Impedovo, S. Abstract We need a vision model in order to process large astronomical images. Classical models, used on many sets of images, failed to bring a complete analysis because they are based on a single scale for the adapted smoothing and for the background mapping. That lead us to introduce a new vision model based on the wavelet transform, which splits the image in scale-space, allowing us to detect objects of different sizes. The discrete wavelet transform is obtained by the so-called "a trous" algorithm. We choose the wavelet function resulting from the difference between two successive B-spline approximations. We define an object as a subtree resulting from the image segmentation in wavelet space. The main steps of the vision procedure are: (i) the wavelet transform of the image, (ii) scale/scale thresholding of the wavelet images, (iii) field labelling of the wavelet images, and (iv) object extraction. We have built a partial reconstruction algorithm which provides an image for each object. It is easy to compute any desired parameter from any object. Our experiments showed that the detection quality is very good, and this method showed low dispersion for photometric measurements Thesaurus astronomical photometry; astronomy computing; computer vision; image reconstruction; image segmentation; wavelet transforms Other Terms partial image reconstruction; astronomical image inventory; discrete wavelet transform; vision model; adapted smoothing; background mapping; scale-space; astronomical object detection; a trous algorithm; successive B-spline approximations; subtree; image segmentation; scale/scale thresholding; field labelling; object extraction; detection quality; dispersion; photometric measurements ClassCodes A9575M; A9575P; A9575D; A4230V; C7350; C5260B; C1130 Article Type Practical; Theoretical / Mathematical; Experimental Language English RecordType Conference ControlNo. 4784587 AbstractNos. A9422-9575-006; C9411-7350-011 ISBN or SBN 981 02 1552 5 References 14 Country Pub. Singapore date 1218 ------------------------------------------------------------ Author Sapiro, G.; Bruckstein, A.M.; Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel Title A B-spline based affine invariant multiscale shape representation Source Proceedings of 7th International Conference on Image Analysis and Processing; Part: Monopoli, Italy; Part: 20-22 Sept. 1993; Proceedings of the 7th International Conference on Image Analysis and Processing. Progress in Image Analysis and Processing III; Singapore; World Scientific; xxiv+733; 1994; pp. 137-44 Editor Impedovo, S. Abstract Multiscale representations constitute an important topic in different fields as computer vision, CAD, and image processing. In this work, a multiscale representation of planar shapes is described. The approach is based on computing B-splines of increasing orders, and therefore is automatically affine invariant. The resulting representation satisfies all the basic scale-space properties at least in a qualitative form, and is simple to implement Thesaurus CAD; computer vision; splines (mathematics) Other Terms B-spline based affine invariant multiscale shape representation; computer vision; CAD; image processing; planar shapes; scale- space properties ClassCodes B6140C; B0290F; C5260B; C4130 Article Type Practical; Theoretical / Mathematical Language English RecordType Conference ControlNo. 4784549 AbstractNos. B9411-6140C-290; C9411-5260B-232 ISBN or SBN 981 02 1552 5 References 23 Country Pub. Singapore date 1218 ------------------------------------------------------------ Author Theimer, W.M.; Mallot, H.A.; Inst. fur Neuroinf., Ruhr-Univ., Bochum, Germany Title Vergence guided depth reconstruction using a phase method Source Proceedings of Sixth International Conference on Neural Networks and their Industrial and Cognitive Applications; Part: Nimes, France; Part: 25-29 Oct. 1993; Sponsored by: AFIA; ARC; IFIP; JSAI & SEE; EERIE; et al; Sixth International Conference. Neural Networks and their Industrial and Cognitive Applications. NEURO-NIMES 93 Conference Proceedings and Exhibition Catalog; Nanterre, France; EC2; 500; 1993; pp. 299-308 Abstract We present a technique to guide vergence movements for an active stereo camera system and to calculate dense disparity maps. Both processes are described in the same theoretical framework based on phase differences in complex Gabor filter responses, modelling receptive field properties in the visual cortex. While the camera movements are computed with input images of coarse spatial resolution, disparity calculation uses a finer resolution in the scale space. The correspondence problem is solved implicitly by restricting the disparity range around zero disparity to the filter kernel sizes (Panum's area in the human visual system). The vergence process is interpreted as a mechanism to minimize global disparity, thereby setting a 3D region of interest for subsequent disparity detection. The disparity map represents smaller local disparities as an important cue for depth perception. Experimental data for vergence in natural scenes and resulting disparity maps are presented Thesaurus image reconstruction; stereo image processing Other Terms depth reconstruction; stereo camera system; dense disparity maps ; Gabor filter responses; visual cortex; disparity calculation; correspondence problem; disparity range; global disparity; natural scenes ClassCodes C5260B; C1250 Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4778274 AbstractNos. C9411-5260B-195 ISBN or SBN 2 906899 95 X References 12 Country Pub. France date 1219 ------------------------------------------------------------ Author Falzon, F.; Giraudon, G.; INRIA, Sophia-Antipolis, France Title Singularity analysis and derivative scale-space Source Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; Part: Seattle, WA, USA; Part: 21-23 June 1994; Sponsored by: IEEE Comput. Soc. Tech. Committee on Pattern Anal. & Machine Intelligence; Proceedings 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.94CH3405-8); Los Alamitos, CA, USA; IEEE Comput. Soc. Press; xvi+1009; 1994; pp. 245-50 Abstract The scale-space representations and wavelet transform theory have provided us with good singularity detection frameworks. Thanks to such methods, image understanding processes have become more and more powerful. Some computer vision tasks also require a knowledge on the nature of detected singularities. We propose, in this paper, to take into account these requirements, and we present a singularity detection method based on fractional calculus arguments Thesaurus computer vision; wavelet transforms Other Terms scale-space representations; wavelet transform; singularity detection; image understanding; computer vision; fractional calculus ClassCodes C1250; C5260B; C1130 Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4777938 AbstractNos. C9411-1250-133 ISBN or SBN 0 8186 5825 8 References 26 U.S. Copyright Clearance Center Code 1063-6919/94/$3.00 Country Pub. USA date 1228 ------------------------------------------------------------ Author Matsopoulos, G.; Marshall, S.; Strathclyde Univ., Glasgow, UK Title Feature migration in morphological scale space Source Proceedings of ICASSP '93; Part: Minneapolis, MN, USA; Part: 27- 30 April 1993; Sponsored by: IEEE Signal Process. Soc; ICASSP-93. 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing (Cat. No.92CH3252-4); New York, NY, USA; IEEE; 5 vol. (652+735+606+559+681); 1993; pp. 599-602 vol.3 Abstract A novel morphological operator for scale space evaluation is introduced. The operator (X/sub B/)/sup B/ is less prone to object shrinkage and signal spreading than the Gaussian approach, and it treats similarly the intrusions and protrusions of the original image. Zero crossing migration with respect to this operation for various types of signal is derived. In order to track events efficiently from scale to scale, it is necessary to understand how the zero crossings of a signal migrate with the scale factor. It is shown that equations can be produced to describe this movement for specific special cases, such as ramp and edges. The new theoretical results can be used to make feature tracking with nonlinear techniques more efficient Thesaurus feature extraction; image processing; mathematical morphology; tracking Other Terms morphological operator; scale space evaluation; object shrinkage ; signal spreading; zero crossings; feature tracking; nonlinear techniques ClassCodes B6140C; C1250; C5260B Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4777723 AbstractNos. B9411-6140C-204; C9411-1250-123 ISBN or SBN 0 7803 0946 4 References 5 U.S. Copyright Clearance Center Code 0 7803 0946 4/93/$3.00 Country Pub. USA date 1213 ------------------------------------------------------------ Author Ogniewicz, R.L.; Commun. Technol. Lab., Swiss Federal Inst. of Technol., Zurich, Switzerland Title Skeleton-space: a multiscale shape description combining region and boundary information Source Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; Part: Seattle, WA, USA; Part: 21-23 June 1994; Sponsored by: IEEE Comput. Soc. Tech. Committee on Pattern Anal. & Machine Intelligence; Proceedings 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.94CH3405-8); Los Alamitos, CA, USA; IEEE Comput. Soc. Press; xvi+1009; 1994; pp. 746-51 Abstract A multiscale extension to the medial axis transform(MAT) or skeleton can be obtained by combining information derived from a scale-space hierarchy of boundary representations with region information provided by the MAT. The skeleton-space is constructed by attributing each skeleton component with a hierarchically ordered sequence of residual values, each expressing the saliency of the component at a distinct resolution level. Since our method amounts to a rather symbolic than iconic computation of a multiscale MAT, it does not introduce the correspondence problem between distinct levels of detail, in contrast to other commonly proposed techniques. Our multiscale MAT is capable of describing complex shapes characterized by significantly jagged boundaries. Furthermore, tracking the evolution of prominent loci of the MAT such as nodes across scales permits to assess the most significant skeleton constituents and to automatically determine pruning parameters. A salient subset of the MAT (first order skeleton) can be extracted without the need of manual threshold adjustment Thesaurus image processing; mathematical morphology Other Terms skeleton-space; multiscale shape description; boundary information; region information; medial axis transform; scale- space hierarchy; boundary representations; hierarchically ordered sequence; pruning parameters; image analysis; mathematical morphology ClassCodes B6140C; C1250; C5260B Article Type Practical; Theoretical / Mathematical Language English RecordType Conference ControlNo. 4771239 AbstractNos. B9411-6140C-098; C9411-1250-080 ISBN or SBN 0 8186 5825 8 References 16 U.S. Copyright Clearance Center Code 1063-6919/94/$3.00 Country Pub. USA date 1228 ------------------------------------------------------------ Author Goshtasby, A.; O'Neill, W.D.; Dept. of Electr. Eng. & Comput. Sci., Chicago Univ., IL, USA Title Curve fitting by a sum of Gaussians Source CVGIP: Graphical Models and Image Processing; CVGIP, Graph. Models Image Process. (USA); vol.56, no.4; July 1994; pp. 281-8 Abstract Gaussians are useful in multiscale representation of video data. An algorithm is presented which approximates a sequence of uniformly spaced single-valued data by a sum of Gaussians with a prescribed accuracy. The scale-space image of the data is used to estimate the number of Gaussians and their initial parameters. The Marquardt algorithm (1963) is then used to optimize the parameters Thesaurus curve fitting; image processing; optimisation; parameter estimation Other Terms curve fitting; Gaussians; multiscale representation; video data ; Marquardt algorithm; initial parameters; scale-space image ClassCodes C5260B; C1250; C1180; C6130B Article Type Theoretical / Mathematical Coden CGMPE5 Language English RecordType Journal ControlNo. 4769682 AbstractNos. C9411-5260B-027 ISSN 10499652 References 8 U.S. Copyright Clearance Center Code 1049-9652/94/$6.00 Country Pub. USA date 1229 ------------------------------------------------------------ Author Tremblay, M.; Savard, M.; Poussart, D.; Dept. of Electr. Eng., Laval Univ., Que., Canada Title Medium level scene representation using a VLSI smart hexagonal sensor with multiresolution edge extraction capability and scale space integration processing Source Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; Part: Seattle, WA, USA; Part: 21-23 June 1994; Sponsored by: IEEE Comput. Soc. Tech. Committee on Pattern Anal. & Machine Intelligence; Proceedings 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.94CH3405-8); Los Alamitos, CA, USA; IEEE Comput. Soc. Press; xvi+1009; 1994; pp. 632-7 Abstract This paper presents a sensing approach where photo-transduction, multi-resolution feature extraction, scale-space integration and edge tracking are performed on a mixed (digital-analog) VLSI architecture in order to generate medium-level scene description. The proposed system is mainly targeted for robot vision applications where feature description is preferred to a set of raw or raster 2D images and edge maps. The Multiport Access photo- Receptor (MAR) is a CMOS sensor and represents the main sensory part of this integrated image acquisition system. VLSI also provides means to integrate analog computing, digital controller and DSP co-processor modules which define a powerful sensory chip set for focal plane image processing. A current version of the MAR sensor which implements 256*256 pixels includes 16 analog spatial filters which simultaneously compute multiresolution edge maps. This unique 2D hexagonal smart sensor approach which performs up to 8.5*10/sup 9/ arithmetic Op/sec during the acquisition/filtering phase and 25*10/sup 9/ Logical Op/sec for scale-space integration allows high resolution image capability. It represents a significant improvement for passive sensory units in a compact assembly for computer vision applications Thesaurus CMOS integrated circuits; computer vision; edge detection; feature extraction; image sensors; spatial filters; VLSI Other Terms medium level scene representation; VLSI smart hexagonal sensor; multiresolution edge extraction capability; scale space integration processing; feature extraction; edge tracking; mixed digital-analog VLSI architecture; robot vision; Multiport Access photo-Receptor; CMOS sensor; image acquisition system; digital controller; DSP co-processor modules; focal plane image processing; analog spatial filters; multiresolution edge maps ClassCodes A4280Q; B7230G; B6140C; B2570D; C1250; C5260B; C3240K Article Type Applications; Practical Language English RecordType Conference ControlNo. 4764238 AbstractNos. A9420-4280Q-004; B9410-7230G-033; C9410-1250-212 ISBN or SBN 0 8186 5825 8 References 12 U.S. Copyright Clearance Center Code 1063-6919/94/$3.00 Country Pub. USA date 1228 ------------------------------------------------------------ Author Jang, B.K.; Health Sci. Res. Lab., Eastman Kodak Co., Rochester, NY, USA Title Morphological scale space for shape smoothing Source Proceedings of the Fourth International Conference on Signal Processing Applications and Technology. ICSPAT '93; Part: vol.1; Part: Santa Clara, CA, USA; Part: 28 Sept.-1 Oct. 1993; Proceedings of the Fourth International Conference on Signal Processing Applications and Technology; Newton, MA, USA; DSP Associates; 2 vol. 1675; 1993; pp. 746-55 vol.1 Abstract In this paper, we describe a multiscale image representation based on morphological operations. A set of boundaries of an object is first smoothed by a number of opening and closing operations each using a different size structuring element, generating the multiple scale representation. Then, depending on specific applications, features of the smoothed boundary are extracted and linked together across a continuum of scales forming a map called the morphological scale space (MSS). Properties of this scale space map are investigated. A shape smoothing algorithm is presented to show how the scale space representation along with an organization scheme could be applied to solve some practical vision problems. Specifically, in line with Witkin's idea of Gaussian scale space filtering, the organization scheme is developed to determine the relationship among various boundary features. As a consequence, the proposed algorithm allows the selection of the perceptually dominant scales for shape smoothing without prior knowledge of the object boundary and its noise. The proposed algorithm uses both region and boundary information, addresses the issues of local and global information, and solves the problems of scale. Moreover, the method is computationally efficient, parameter free, and stable Thesaurus computer vision; feature extraction; mathematical morphology Other Terms morphological scale space; shape smoothing; multiscale image representation; closing operations; opening operations; structuring element; smoothed boundary; feature extraction; scale space map; practical vision problems; Gaussian scale space filtering; boundary features; perceptually dominant scales ClassCodes B6140C; C1250; C5260B Article Type Practical; Theoretical / Mathematical; Experimental Language English RecordType Conference ControlNo. 4759476 AbstractNos. B9410-6140C-309; C9410-1250-171 References 12 Country Pub. USA date 1218 ------------------------------------------------------------ Author Jones, A.G.; Taylor, C.J.; Dept. of Med. Biophys., Manchester Univ., UK Title Robust shape from shading Source Image and Vision Computing; Image Vis. Comput. (UK); vol.12, no.7; Sept. 1994; pp. 411-21 Abstract Existing shape from shading algorithms are not robust enough to work reliably with real images. We present a new paradigm for shape from shading based upon a scale space surface representation. Using this representation, the shape from shading problem becomes one of forming a surface from Gaussian basis functions in such a way that the shading in the original image is explained. The algorithm is extremely robust, producing convincing results even when there is considerable noise present. In its current form, the algorithm requires the reflectance function to be approximately circularly symmetric. We show results for synthetic images, both with and without added noise, and for real scanning electron microscope images Thesaurus computer vision; image recognition; noise Other Terms algorithms; real images; scale space surface representation; robust shape from shading; surface; Gaussian basis functions; noise; reflectance function; synthetic images; scanning electron microscope images ClassCodes C5260B Article Type Practical Coden IVCODK Language English RecordType Journal ControlNo. 4756038 AbstractNos. C9410-5260B-157 ISSN 02628856 References 15 U.S. Copyright Clearance Center Code 0262-8856/94/07/0411-11$10.00 Country Pub. UK date 1231 ------------------------------------------------------------ Author Kulkarni, A.; Shevgaonkar, R.; Sahasrabudhe, S.C.; Dept. of Electr. Eng., Indian Inst. of Technol., Bombay, India Title Edge detection using scale space knowledge Source Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation; Part: vol.2; Part: Beijing, China; Part: 19-21 Oct. 1993; Proceedings TENCON '93. 1993 IEEE Region 10 Conference on 'Computer, Communication, Control and Power Engineering' (Cat. No. 93CH3286-2); New York, NY, USA; IEEE; 5 vol. (xxvi+1206+xvii+676+xv+580+xvii+619); 1993; pp. 986-90 vol.2 Editor Yuan Baozong Abstract Edge detection has acquired enormous importance in computer vision research: gaussian filter has been widely used in the past to accomplish this task. Although the gaussian filter has nice scaling behaviour and is computationally elegant, it suffers from the disadvantage of delocalisation of edges when operated at higher scales. Multi-scale processing of an image is thus required. In this paper, the process of edge detection has been looked upon as a reasoning problem. In the past, knowledge handling and reasoning have been attributed to high level vision routines, but from the results presented here, it can be argued that reasoning does play an important role in the process of edge detection. The knowledge base required for this is formed out of the theory of scale space. Particular emphasis is laid on the behaviour of delocalised, missing and false or spurious edges in the scale space. The scale space is formed by operating the LoG filter of different sizes on the input image. The dissimilarity among the zero crossings is measured across the scales. It is useful in the removal of false edges. A compatibility measure relates the zero-crossing contour with the current scale parameter, sigma . The rules are coded in NEXPERT OBJECT 2.0. The results are compared with Canny's multiple edge detection algorithm (J.F. Canny, 1986) Thesaurus computer vision; edge detection; inference mechanisms; knowledge based systems Other Terms edge detection; scale space knowledge; computer vision research; gaussian filter; scaling behaviour; multi-scale processing; image processing; reasoning problem; knowledge handling; reasoning; knowledge based systems; spurious edges; LoG filter; zero-crossing contour; NEXPERT OBJECT 2.0; multiple edge detection algorithm ClassCodes B6140C; C5260B; C6170 Article Type Practical; Theoretical / Mathematical Language English RecordType Conference ControlNo. 4750929 AbstractNos. B9410-6140C-204; C9410-5260B-121 ISBN or SBN 0 7803 1233 3 References 16 Country Pub. USA date 1219 ------------------------------------------------------------ Author Fermuller, C.; Kropatsch, W.; Comput. Vision Lab., Maryland Univ., College Park, MD, USA Title A syntactic approach to scale-space-based corner description Source IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Trans. Pattern Anal. Mach. Intell. (USA); vol.16, no.7; July 1994; pp. 748-51 Abstract Planar curves are described by information about corners integrated over various levels of resolution. The detection of corners takes place on a digital representation. To compensate for ambiguities arising from sampling problems due to the discreteness, results about the local behavior of curvature extrema in continuous scale-space are employed Thesaurus edge detection; image processing; parallel processing Other Terms syntactic approach; scale space based corner description; planar curves; resolution; corner detection; sampling problems; curvature extrema ClassCodes B6140C; C1250 Article Type Theoretical / Mathematical Coden ITPIDJ Language English RecordType Journal ControlNo. 4742145 AbstractNos. B9410-6140C-083; C9410-1250-056 ISSN 01628828 References 23 U.S. Copyright Clearance Center Code 0162-8828/94/$04.00 Country Pub. USA date 1229 ------------------------------------------------------------ Author Jianqing Liu; Yee-Hong Yang; Comput. Vision Lab., Saskatchewan Univ., Saskatoon, Sask., Canada Title Multiresolution color image segmentation Source IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Trans. Pattern Anal. Mach. Intell. (USA); vol.16, no.7; July 1994; pp. 689-700 Abstract Image segmentation is the process by which an original image is partitioned into some homogeneous regions. In this paper, a novel multiresolution color image segmentation (MCIS) algorithm which uses Markov random fields (MRF's) is proposed. The proposed approach is a relaxation process that converges to the MAP (maximum a posteriori) estimate of the segmentation. The quadtree structure is used to implement the multiresolution framework, and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore, maximize the MAP estimate. The multiresolution scheme enables the use of different dissimilarity measures at different resolution levels. Consequently, the proposed algorithm is noise resistant. Since the global clustering information of the image is required in the proposed approach, the scale space filter (SSF) is employed as the first step. The multiresolution approach is used to refine the segmentation. Experimental results of both the synthesized and real images are very encouraging. In order to evaluate experimental results of both synthesized images and real images quantitatively, a new evaluation criterion is proposed and developed Thesaurus image segmentation; Markov processes; simulated annealing; tree data structures Other Terms multiresolution color image segmentation; Markov random fields; relaxation process; maximum a posteriori estimate; quadtree structure; simulated annealing; splitting; merging; dissimilarity measures; noise resistant; global clustering information; scale space filter; real images; synthesized images; evaluation criterion ClassCodes B6140C; B0260; B0240Z; C1250; C5260B; C1180; C1140Z Article Type Practical; Theoretical / Mathematical Coden ITPIDJ Language English RecordType Journal ControlNo. 4742138 AbstractNos. B9410-6140C-078; C9410-1250-051 ISSN 01628828 References 19 U.S. Copyright Clearance Center Code 0162-8828/94/$04.00 Country Pub. USA date 1229 ------------------------------------------------------------ Author Pizer, S.M.; Burbeck, C.A.; Coggins, J.M.; Fritsch, D.S.; Morse, B.S.; Medical Image Display Res. Group, North Carolina Univ., Chapel Hill, NC, USA Title Object shape before boundary shape: scale-space medial axes Source Journal of Mathematical Imaging and Vision; J. Math. Imaging Vis. (Netherlands); vol.4, no.3; July 1994; pp. 303-13 Abstract Representing object shape in two or three dimensions has typically involved the description of the object boundary. This paper proposes a means for characterizing object structure and shape that avoids the need to find an explicit boundary. Rather, it operates directly from the image-intensity distribution in the object and its background, using operators that respond to "boundariness." It produces a sort of medial-axis description that recognizes that both axis location and object width must be defined according to a tolerance proportional to the object width. This generalized axis is called the multiscale medial axis because it is defined as a curve or set of curves in scale space. It has all of the advantages of the traditional medial axis. It also has significant new advantages: it does not require a predetermination of exactly what locations are included in the object, it provides gross descriptions that are stable against image detail, and it can be used to identify subobjects and regions of boundary detail and to characterize their shape properties Thesaurus edge detection; image processing Other Terms boundary shape; scale space medial axes; object boundary; object structure; object shape; image intensity distribution; medial axis description; multiscale medial axis; image analysis; shape description ClassCodes B6140C; C1250 Article Type Theoretical / Mathematical Coden JIMVEC Language English RecordType Journal ControlNo. 4739197 AbstractNos. B9410-6140C-034; C9410-1250-023 ISSN 09249907 References 26 U.S. Copyright Clearance Center Code 0924-9907/94/$5.00 Country Pub. Netherlands date 1229 ------------------------------------------------------------ Author Yoon, S.H.; Alexander, W.E.; Kim, J.H.; Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA Title Scale-invariant shape representation based on the wavelet transform Source Signal Processing, Sensor Fusion, and Target Recognition III; Part : Orlando, FL, USA; Part: 4-6 April 1994; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2232; 1994; pp. 142-53 Abstract Matching of occluded objects is a difficult problem. Moreover, the problem is more difficult when scale-invariant matching is needed. A scale invariant representation is essential for this application. In this paper, we propose using the wavelet transform of a boundary to obtain a scale-invariant representation. We use the cubic B-spline as a smoothing function of the wavelet transform since the B-spline is analytically well defined and simple to implement. We implement the fast continuous wavelet transform by using a dyadic wavelet decomposition and dilated B-splines. As a result of using the wavelet transform, we obtain boundaries at various scales while using a small number of data points. The existing scale-space image approaches are not effective for occluded object matching since they use a normalized x-axis and too many data points. We propose a new scale-invariant representation similar to the scale-space image. The representation is generated by locating zero-crossings of the curvature function of boundaries at only the scales where the number of zero-crossings is changing. We scale the x-axis for each scale instead of using the same normalization for all scales. The proposed representation is scale-invariant and appropriate for scale-invariant matching with occlusion Thesaurus image recognition; invariance; splines (mathematics); wavelet transforms Other Terms scale-invariant shape representation; dyadic wavelet decomposition; occluded objects; scale-invariant matching; cubic B-spline; smoothing function; fast continuous wavelet transform; dilated B-splines; scale-space image approaches; curvature function ClassCodes B6140C; C1250; C5260B Article Type Theoretical / Mathematical Coden PSISDG Language English RecordType Conference ControlNo. 4737080 AbstractNos. B9410-6140C-007; C9410-1250-005 ISSN 0277786X References 21 U.S. Copyright Clearance Center Code 0 8194 1536 7/94/$6.00 Country Pub. USA date 1226 ------------------------------------------------------------ Author ter Haar Romeny, B.M.; Florack, L.M.J.; Salden, A.H.; Viergever, M.A.; 3D Comput. Vision Res. Group, Utrecht Univ. Hospital, Netherlands Title Higher order differential structure of images Source Image and Vision Computing; Image Vis. Comput. (UK); vol.12, no.6; July-Aug. 1994; pp. 317-25 Abstract This paper is meant as a tutorial on the basic concepts for vision in the 'Koenderink' school. The concept of scale-space is a necessity, if the extraction of structure from measured physical signals (i.e. images) is at stage. The Gaussian derivative kernels for physical signals are then the natural analogues of the mathematical differential operators. This paper discusses some interesting properties of the Gaussian derivative kernels, like their orthogonality and behaviour with noisy input data. Geometrical structure to extract is expressed in terms of differential invariants, in this paper limited to invariants under orthogonal transformations. Three representations are summarized: cartesian, gauge and manifest invariant notation. Many explicit examples are given. A section is included about the computer implementation of the calculation of higher order invariant structure Thesaurus computer vision; feature extraction Other Terms higher order differential image structure; measured physical signals; Gaussian derivative kernels; mathematical differential operators; manifest invariant notation; computer implementation; higher order invariant structure ClassCodes B6140C; C1250; C5260B Article Type Practical; Theoretical / Mathematical Coden IVCODK Language English RecordType Journal ControlNo. 4727494 AbstractNos. B9409-6140C-246; C9409-1250-155 ISSN 02628856 References 36 U.S. Copyright Clearance Center Code 0262-8856/94/06/0317-09$10.00 Country Pub. UK date 1229 ------------------------------------------------------------ Author Worring, M.; Smeulders, A.W.M.; Dept. of Math. & Comput. Sci., Amsterdam Univ., Netherlands Title Shape of an arbitrary finite point set in IR/sup 2/ Source Journal of Mathematical Imaging and Vision; J. Math. Imaging Vis. (Netherlands); vol.4, no.2; May 1994; pp. 151-70 Abstract Studies the shape of a finite point set in IR/sup 2/, where the points are not bound to a regular grid like Z/sup 2/. The shape of a connected point set in IR/sup 2/ is captured by its boundary. For a finite point set the boundary is a directed graph that connects points identified as boundary points. The authors argue that to serve as a proper boundary definition the directed graph should regulate scale, be minimal, have an increasing interior and be consistent with the boundary definition of connected objects. The authors propose to use the directed variant of the alpha -shape as defined by Edelsbrunner et al. (1983), which the authors call the alpha -graph. The alpha -graph is based on a generalization of the convex hull. The computational aspects of the alpha -graph have been extensively studied, but little attention has been paid to the potential use of the alpha -graph as a shape descriptor or a boundary definition. In this paper the authors prove that the alpha -graph satisfies the aforementioned criteria. The authors also prove a relation between the alpha - graph and the opening scale space from mathematical morphology. In fact, the alpha -hull provides a generalization of this scale space Thesaurus computational geometry; directed graphs; image processing; mathematical morphology Other Terms arbitrary finite point set; IR/sup 2/; connected point set; directed graph; boundary definition; alpha -shape; alpha - graph; convex hull; shape descriptor; mathematical morphology ClassCodes B6140C; B0250; C1250; C1160; C5260B Article Type Practical; Theoretical / Mathematical Coden JIMVEC Language English RecordType Journal ControlNo. 4724559 AbstractNos. B9409-6140C-218; C9409-1250-133 ISSN 09249907 References 16 U.S. Copyright Clearance Center Code 0924-9907/94/$5.00 Country Pub. Netherlands date 1227 ------------------------------------------------------------ Author El-Fallah, A.I.; Ford, G.E.; CIPIC, California Univ., Davis, CA, USA Title Nonlinear adaptive image filtering based on inhomogeneous diffusion and differential geometry Source Image and Video Processing II; Part: San Jose, CA, USA; Part: 7- 9 Feb. 1994; Sponsored by: SPIE; IS&T; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2182; 1994; pp. 49-63 Abstract The inadequacy of the classic linear approach to edge detection and scale space filtering lies in the spatial averaging of the Laplacian. The Laplacian is the divergence of the gradient and thus is the divergence of both magnitude and direction. The divergence in magnitude characterizes edges and this divergence must not be averaged if the image structure is to be preserved. We introduce a new nonlinear filtering theory that that only averages the divergence of direction. This averaging keeps edges and lines intact as their direction is nondivergent. Noise does not have this nondivergent consistency and its divergent direction will be averaged. Higher order structures such as corners are singular points or inflection points in the divergence of direction and also will be averaged. Corners are intersection points of edges of nondivergent direction (or smooth curves of small divergence in direction) and their averaging will be limited. This approach provides a better compromise between noise removal and preservation of image structure. Experiments that verify and demonstrate the adequacy of this new theory are presented Thesaurus adaptive filters; differential geometry; edge detection; filtering and prediction theory; noise Other Terms nonlinear adaptive image filtering; inhomogeneous diffusion; differential geometry; edge detection; scale space filtering; nonlinear filtering theory; divergence of direction; nondivergent consistency; corners; singular points; inflection points; noise removal; image structure preservation; spatial averaging ClassCodes B6140C; C5260B; C1250 Article Type Theoretical / Mathematical; Experimental Coden PSISDG Language English RecordType Conference ControlNo. 4716779 AbstractNos. B9409-6140C-077; C9409-5260B-054 ISSN 0277786X References 16 U.S. Copyright Clearance Center Code 0 8194 1477 8/94/$6.00 Country Pub. USA date 1224 ------------------------------------------------------------ Author Peters, R.A., II; Nichols, J.A.; Dept. of Electr. Eng., Vanderbilt Univ., Nashville, TN, USA Title Morphological bandpass decomposition of images Source Nonlinear Image Processing V; Part: San Jose, CA, USA; Part: 7- 9 Feb. 1994; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2180; 1994; pp. 163-74 Abstract The concept of a morphological size distribution is well known. It can be envisioned as a sequence of progressively more highly smoothed images which is a nonlinear analogue of scale space. Whereas the differences between Gaussian lowpass filtered images in scale space form a sequence of approximately Laplacian bandpass filtered images, the difference image sequence from a morphological size distribution is not bandpass in any usual sense for most images. This paper presents a proof that a strictly size band limited sequence can be created along one dimension in an n dimensional image. This result is used to show how an image time sequence can be decomposed into a set of sequences each of which contains only events of a specific limited duration. It is shown that this decomposition can be used for noise reduction. This paper also presents two algorithms which create from morphological size distributions, (pseudo) size bandpass decompositions in more than one dimension. One algorithm uses Vincent grayscale reconstruction on the size distribution. The other reconstructs the difference image sequence Thesaurus image reconstruction; image sequences; mathematical morphology; random noise; statistical analysis Other Terms morphological bandpass decomposition; morphological size distribution; smoothed image sequences; scale space; difference image sequence; band limited sequence; image time sequence; noise reduction; bandpass decompositions; Vincent grayscale reconstruction; size distribution ClassCodes B6140C; C5260B Article Type Theoretical / Mathematical; Experimental Coden PSISDG Language English RecordType Conference ControlNo. 4716757 AbstractNos. B9409-6140C-057; C9409-5260B-037 ISSN 0277786X References 8 U.S. Copyright Clearance Center Code 0 8194 1475 1/94/$6.00 Country Pub. USA date 1224 ------------------------------------------------------------ Author Bangham, J.A.; Sch. of Inf. Syst., East Anglia Univ., Norwich, UK Title Median and morphological scale space filtering and zero-crossings Source Nonlinear Image Processing V; Part: San Jose, CA, USA; Part: 7- 9 Feb. 1994; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2180; 1994; pp. 90-8 Abstract This paper investigates nonlinear filter sequences (filters in series, known as sieves) or banks (in parallel) as a means of achieving multiscale decomposition. Recently multiscale decomposition using both erosion (dilation) and closing (opening) operations with sets of increasing scale flat structuring elements have been used to analyse edges over multiple scales and the granularity of images. These do not introduce new edges as scale increases. However, they are not at all statistically robust in the face of, for example, salt and pepper noise. This paper shows that sieves also do not introduce new edges, are very robust and perform at least as well as discreet Gaussian filters when applied to sampled data. Analytical support for these observations is provided by the morphology decomposition theorem Thesaurus edge detection; filtering and prediction theory; mathematical morphology; nonlinear systems Other Terms morphological scale space filtering; median scale space filtering ; zero-crossings; multiscale decomposition; scale space channels; nonlinear filter sequences; erosion operations; dilation operations; closing operation; opening operations; edge analysis; salt and pepper noise; sampled data; morphology decomposition theorem ClassCodes B6140C; C5260B; C1250 Article Type Experimental Coden PSISDG Language English RecordType Conference ControlNo. 4716750 AbstractNos. B9409-6140C-051; C9409-5260B-030 ISSN 0277786X References 15 U.S. Copyright Clearance Center Code 0 8194 1475 1/94/$6.00 Country Pub. USA date 1224 ------------------------------------------------------------ Author Miller, E.L.; Willsky, A.S.; Lab. for Inf. & Decision Syst., MIT, Cambridge, MA, USA Title Wavelet transforms and multiscale estimation techniques for the solution of multisensor inverse problems Source Wavelet Applications; Part: Orlando, FL, USA; Part: 5-8 April 1994; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2242; 1994; pp. 28-39 Abstract The application of multiscale and stochastic techniques to the solution of linear inverse problems is presented. This approach allows for the explicit and easy handling of a variety of difficulties commonly associated with problems of this type. Regularization is accomplished via the incorporation of prior information in the form of a multiscale stochastic model. We introduce the relative error covariance matrix (RECM) as a tool for quantitatively evaluating the manner in which data contributes to the structure of a reconstruction. In particular, the use of a scale space formulation is ideally suited to the fusion of data from several sensors with differing resolutions and spatial coverage (e.g. sparse or limited availability). Moreover, the RECM both provides us with an ideal tool for understanding and analyzing the process of multisensor fusion and allows us to define the space-varying optimal scale for reconstruction as a function of the nature (resolution, quality, and coverage) of the available data. Examples of our multiscale maximum posteriori inversion algorithm are demonstrated using a two channel deconvolution problem Thesaurus estimation theory; inverse problems; matrix algebra; sensor fusion; stochastic processes; wavelet transforms Other Terms multiscale estimation techniques; multisensor inverse problems; stochastic techniques; linear inverse problems; regularization; multiscale stochastic model; relative error covariance matrix; RECM; scale space formulation; data fusion; multisensor fusion; space-varying optimal scale for reconstruction; multiscale maximum posteriori inversion algorithm; channel deconvolution problem ClassCodes B6140; B0210; C1260; C1110 Article Type Theoretical / Mathematical Coden PSISDG Language English RecordType Conference ControlNo. 4707528 AbstractNos. B9408-6140-144; C9408-1260-111 ISSN 0277786X References 10 U.S. Copyright Clearance Center Code 0 8194 1546 4/94/$6.00 Country Pub. USA date 1226 ------------------------------------------------------------ Author Bala, J.W.; Wechsler, H.; Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA Title Learning to detect targets using scale-space and genetic search Source Proceedings of ICGA-93: Fifth International Conference on Genetic Algorithms; Part: Urbana-Champaign, IL, USA; Part: 17-22 July 1993; Proceedings of the Fifth International Conference on Genetic Algorithms; San Mateo, CA, USA; Morgan Kaufmann; xii+665; 1993; pp. 516-22 Editor Forrest, S. Abstract Introduces a novel methodology for target detection using scale- space and genetic algorithms (GAs). High-performance detection operators, defined as 2D masks, are derived using GAs operating at different levels of the Gaussian pyramid. The population of operators, iteratively evaluated according to a statistical performance index corresponding to target recognition ability, evolves into an optimal set of scale-space masks using the evolution principles of genetic search. Experimental results of target detection in poor quality and noisy infrared images are presented to illustrate the merit and feasibility of the proposed method Thesaurus genetic algorithms; image recognition; iterative methods; learning (artificial intelligence); performance index; search problems Other Terms learning; scale-space image representation; high performance detection operators; noisy IR images; iterative evaluation; poor quality images; genetic search; target detection; genetic algorithms; 2D masks; Gaussian pyramid; statistical performance index; target recognition ability; evolution ClassCodes C1250; C5260B; C1180; C1240 Article Type Practical; Theoretical / Mathematical; Experimental Language English RecordType Conference ControlNo. 4706214 AbstractNos. C9408-1250-105 ISBN or SBN 1 558 60299 2 References 8 Country Pub. USA date 1216 ------------------------------------------------------------ Author De Graaf, C.N.; Koster, A.S.E.; Vincken, K.L.; Viergever, M.A.; Comput. Vision Res. Group, Utrecht Univ., Netherlands Title Validation of the interleaved pyramid for the segmentation of 3D vector images Source Pattern Recognition Letters; Pattern Recognit. Lett. (Netherlands); vol.15, no.5; May 1994; pp. 469-75 Abstract A multiresolution pyramid with double scale space sampling (compared to the Burt & Hong scheme, 1981) for the segmentation of 3D images, of which the elements are multiple valued, is described. Evaluation is carried out by quality constrained cost analysis Thesaurus image segmentation Other Terms interleaved pyramid; 3D vector image segmentation; multiresolution pyramid; double scale space sampling; multiple- valued images; quality constrained cost analysis ClassCodes B6140C; C1250 Article Type Theoretical / Mathematical Coden PRLEDG Language English RecordType Journal ControlNo. 4697507 AbstractNos. B9408-6140C-054; C9408-1250-032 ISSN 01678655 References 10 U.S. Copyright Clearance Center Code 0167-8655/94/$07.00 Country Pub. Netherlands date 1227 ------------------------------------------------------------ Author Prasad, K.V.; Balaram, J.; California Inst. of Technol., Pasadena, CA, USA Title Automated inspection for remote telerobotic operations Source Proceedings of 1993 IEEE International Conference on Robotics and Automation; Part: Atlanta, GA, USA; Part: 2-6 May 1993; Sponsored by: IEEE; Proceedings IEEE International Conference on Robotics and Automation (Cat. No.93CH3247-4); Los Alamitos, CA, USA; IEEE Comput. Soc. Press; 3 vol. (xviii+1051+xvi+848+xviii+1042); 1993; pp. 883-8 vol.3 Abstract Automated inspection techniques being developed for surface inspection of remote space platforms are reported. The unique problems of performing visual telerobotic inspection in space are identified. An image differencing method to detect changes to surfaces over a period of time is presented, together with a scale-space technique for flaw identification. Examples from images of laboratory mockups of space platform modules are presented to illustrate the results Thesaurus aerospace control; automatic optical inspection; mobile robots; space vehicles; telecontrol Other Terms remote telerobotic operations; surface inspection; space platforms; visual telerobotic inspection; image differencing method; scale-space technique; flaw identification ClassCodes C3360L; C3250; C3390 Article Type Applications; Practical Language English RecordType Conference ControlNo. 4687369 AbstractNos. C9407-3360L-041 ISBN or SBN 0 8186 3450 2 References 9 U.S. Copyright Clearance Center Code 0 8186 3450 2/93/$3.00 Country Pub. USA date 1214 ------------------------------------------------------------ Author Brown, G.; Forte, P.; Malyan, R.; Barnwell, P.; Mercury Commun., UK Title Perceptual feature based object recognition for automatic inspection Source Machine Vision Applications, Architectures, and Systems Integration II; Part: Boston, MA, USA; Part: 7-9 Sept. 1993; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2064; 1993; pp. 307-18 Abstract This paper describes the development of a practical object recognition framework for industrial inspection applications. The framework is being used to develop a prototype automatic inspection system for surface mounted electronic assemblies. The recognition technique uses the object oriented paradigm to both process and model the image information. Three inter-linked information structures, that allow both image and object modelling information to be represented at multiple resolutions, are described. The first structure is a hierarchical feature based description constructed from the recursive grouping of perceptual features. The second structure provides a containment tree to describe the connections between sub-component parts of a complex object. The final structure is the inheritance tree that provides the capability to model multiple variations of the same object type. The inspection module begins by processing image information from a newly developed nonlinear shape abstraction technique. The information is delivered in the form of a multi- resolution scale space description of the captured image Thesaurus assembling; automatic optical inspection; computer vision; feature extraction; printed circuit manufacture; surface mount technology Other Terms perceptual feature based object recognition; automatic inspection ; industrial inspection; surface mounted electronic assemblies; interlinked information structures; object modelling; hierarchical feature based description; recursive grouping; containment tree; image processing; inspection module; nonlinear shape abstraction; multiresolution scale space description ClassCodes B0170L; B2210D; B6140C; C5260B; C1250; C3355F Article Type Practical Coden PSISDG Language English RecordType Conference ControlNo. 4670634 AbstractNos. B9406-0170L-019; C9406-5260B-158 ISSN 0277786X References 12 U.S. Copyright Clearance Center Code 0 8194 1329 1/93/$6.00 Country Pub. USA date 1218 ------------------------------------------------------------ ------------------------------------------------------------ Author Bichsel, M.; Pentland, A.P.; Media Lab., MIT, Cambridge, MA, USA Title Human face recognition and the face image set's topology Source CVGIP: Image Understanding; CVGIP, Image Underst. (USA); vol.59, no.2; March 1994; pp. 254-61 Abstract Considering an n*n image as an n/sup 2/-dimensional vector, then images of faces can be considered as points in this n/sup 2/- dimensional image space. Previous studies of physical transformations of the face, including translation, small rotations, and illumination changes, showed that the set of face images consists of relatively simple connected subregions in image space. Consequently linear matching techniques can be used to obtain reliable face recognition. However, for more general transformations, such as large rotations or scale changes, the face subregions become highly non-convex. We have therefore developed a scale-space. matching technique that allows us to take advantage of knowledge about important geometrical transformations and about the topology of the face subregion in image space. While recognition of faces is the focus of the paper, the algorithm is sufficiently general to be applicable to a large variety of object recognition tasks Thesaurus face recognition; topology Other Terms face recognition; face image; geometrical transformations; topology; object recognition; physical transformations; small rotations ClassCodes C5260B; C1250; C1160 Article Type Theoretical / Mathematical Coden CIUNEJ Language English RecordType Journal ControlNo. 4668583 AbstractNos. C9406-5260B-137 ISSN 10499660 References 25 U.S. Copyright Clearance Center Code 1049-9660/94/$6.00 Country Pub. USA date 1225 ------------------------------------------------------------ Author Goshtasby, A.; Dept. of Electr. Eng. & Comput. Sci., Illinois Univ., Chicago, IL, USA Title On edge focusing (scale-space imaging) Source Image and Vision Computing; Image Vis. Comput. (UK); vol.12, no.4; May 1994; pp. 247-56 Abstract An algorithm is described that can track edges determined by the Laplacian of Gaussian operator from low to high resolution. The algorithm does not require a precomputed scale step size, but rather determines the step size adaptively by using the image content. A binary search is carried out to find the topology of the scale-space image, which is then used to track the edges. The proposed algorithm guarantees correct edge tracking with step sizes considerably larger than has been previously used Thesaurus edge detection Other Terms edge focusing; Gaussian operator; image content; binary search; scale-space image ClassCodes B6140C; C1250; C5260B Article Type Practical; Theoretical / Mathematical Coden IVCODK Language English RecordType Journal ControlNo. 4666552 AbstractNos. B9406-6140C-157; C9406-1250-111 ISSN 02628856 References 16 U.S. Copyright Clearance Center Code 0262-8856/94/040247-10$10.00 Country Pub. UK date 1227 ------------------------------------------------------------ Author Ray, B.K.; Ray, K.S.; Electron. & Commun. Sci. Unit, Indian Stat. Inst., Calcutta, India Title A new approach to scale-space image Source Pattern Recognition Letters; Pattern Recognit. Lett. (Netherlands); vol.15, no.4; April 1994; pp. 365-72 Abstract This paper presents a new approach to scale-space image based on the authors' (1991) work on polygonal approximation. The polygonal approximation involves a counter which can be varied to produce different sets of vertex points and hence different polygonal approximations. These polygons are combined to construct a scale-space image. The scale-space image is in conformity with the scale-space theory. The scale-space behaviour of different corner models is also analysed and a method for corner detection is proposed. The validity of the proposed technique is tested through experimental results Thesaurus edge detection; finite difference methods; image processing; statistical analysis Other Terms scale-space image; polygonal approximation; vertex points; corner models; corner detection ClassCodes B6140C; B0290P; B0240Z; C1250; C4170; C1140Z Article Type Theoretical / Mathematical Coden PRLEDG Language English RecordType Journal ControlNo. 4666484 AbstractNos. B9406-6140C-151; C9406-1250-106 ISSN 01678655 References 9 U.S. Copyright Clearance Center Code 0167-8655/94/$07.00 Country Pub. Netherlands date 1226 ------------------------------------------------------------ Author Ter Haar Romeny, B.M.; Florack, L.M.J.; Salden, A.H.; Viergever, M.A.; 3D Computer Vision Res. Group, Utrecht Univ. Hospital, Netherlands Title Higher order differential structure of images Source Proceedings of 13th International Conference on Information Processing in Medical Imaging; Part: Flagstaff, AZ, USA; Part: 14-18 June 1993; Information Processing in Medical Imaging. 13th International Conference, IPMI '93 Proceedings; Berlin, Germany; Springer-Verlag; xvi+567; 1993; pp. 77-93 Editor Barrett, H.H.; Gmitro, A.F. Abstract Presents a tutorial on the basic concepts for vision in the 'Koenderink' school. The concept of scale-space is a necessity if the extraction of structure from measured physical signals (i.e. images) is being done. The Gaussian derivative kernels then are, for physical signals, the natural analogs of the mathematical differential operators. This paper discusses some interesting properties of the Gaussian derivative kernels, like their orthogonality and behaviour with noisy input data. Geometrical structure to be extracted is expressed as differential invariants, in this paper limited to invariants under orthogonal transformations. Three representations are summarized: Cartesian, gauge and manifest invariant notation. Many explicit examples are given. A section is included about computer implementation of the calculation of higher order invariant structure Thesaurus computer vision; geometry; image processing Other Terms higher order differential image structure; computer vision; geometrical structure extraction; Cartesian representation; gauge representation; scale-space; Gaussian derivative kernels; orthogonality; noisy input data; manifest invariant notation; higher order invariant structure ClassCodes B6140C; C1250; C5260B Article Type General or Review; Practical Language English RecordType Conference ControlNo. 4648047 AbstractNos. B9405-6140C-252; C9405-1250-180 ISBN or SBN 3 540 56800 X References 35 Country Pub. Germany date 1215 ------------------------------------------------------------ Author Brown, G.; Forte, P.; Malyan, R.; Mercury Commun. Ltd., London, UK Title Non-linear shape abstraction for automatic inspection Source Vision Geometry II; Part: Boston, MA, USA; Part: 9-10 Sept. 1993 ; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2060; 1993; pp. 125-32 Abstract The authors describe the implementation of a non-linear 2D shape abstraction technique. The abstraction procedure systematically simplifies the shape's description using a group of predefined 'rewrite rules'. This procedure operates on a new compact and efficient two dimensional shape representation. The abstraction technique generates a detailed scale space representation of the shape being abstracted. The authors provide a method for analysing this representation to determine the significant shape descriptions. These significant descriptions are used to produce a reduced scale space description of the shape, for use by an object recognition module. The work described will form a module in a system being developed at Kingston University, to inspect surface mounted technology (SMT) circuit boards for defects. These defects will include poor solder joint quality, component placement faults, missing components, etc. The inspection system uses images, captured from a CCD camera, which are initially processed using a transputer. Early test results are presented to show the benefits of this technique in comparison with Gaussian smoothing Thesaurus circuit analysis computing; computer vision; edge detection; inspection; surface mount technology Other Terms automatic inspection; non-linear 2D shape abstraction; abstraction procedure; predefined rewrite rules; two dimensional shape representation; scale space representation; reduced scale space description; object recognition module; surface mounted technology circuit boards; solder joint quality; component placement faults; CCD camera; transputer ClassCodes B1130B; C7410D; C5260B Article Type Practical Coden PSISDG Language English RecordType Conference ControlNo. 4626930 AbstractNos. B9405-1130B-001; C9405-7410D-006 ISSN 0277786X References 7 U.S. Copyright Clearance Center Code 0 8194 1325 9/93/$6.00 Country Pub. USA date 1218 ------------------------------------------------------------ Author Theimer, W.M.; Mallot, H.A.; Tolg, S.; Inst. fur Neuroinf., Ruhr-Univ., Bochum, Germany Title Phase method for binocular vergence control and depth reconstruction Source Intelligent Robots and Computer Vision XI: Biological, Neural Net and 3-D Methods; Part: Boston, MA, USA; Part: 18-20 Nov. 1992; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.1826; 1992; pp. 76-87 Abstract We present a technique to guide vergence movements for an active stereo camera system and to construct dense disparity maps. Both processes are described in the same theoretical framework based on phase differences in complex Gabor filter responses, modelling receptive field properties in the visual cortex. While the camera movements are computed with coarse spatial resolution input images, disparity calculation uses finer resolutions in a scale space. The correspondence problem is solved implicitly by restricting the disparity range around zero disparity to the filter kernel sizes (Panum's area in the human visual system). The method contrasts to matching algorithms-that require an explicit search for correspondence-and to correlation, needing a maximum detection in the correlation function. The vergence process is interpreted as a mechanism to minimize global disparity, thereby setting a 3D region of interest for subsequent disparity detection. This small volume is centered around the fixation point where both optical axes intersect. Additionally it produces a scalar distance measure via vergence angles and camera base. The disparity map represents smaller local disparities as an important cue for depth perception. The vergence control works in a real-time feedback loop. Quantitative results are presented Thesaurus feedback; filtering and prediction theory; stereo image processing Other Terms phase method; binocular vergence control; depth reconstruction; active stereo camera system; complex Gabor filter responses; visual cortex; coarse spatial resolution input images; filter kernel sizes; Panum's area; global disparity minimization; fixation point; depth perception; real-time feedback loop ClassCodes B6140C; C1250; C1260 Article Type Theoretical / Mathematical Coden PSISDG Language English RecordType Conference ControlNo. 4626626 AbstractNos. B9405-6140C-007; C9405-1250-008 ISSN 0277786X References 13 U.S. Copyright Clearance Center Code 0 8194 1027 6/92/$4.00 Country Pub. USA date 1207 ------------------------------------------------------------ Author Chaney, R.D.; Artificial Intelligence Lab., MIT, Cambridge, MA, USA Title Computation of the medial axis skeleton at multiple complexities Source Intelligent Robots and Computer Vision XI: Algorithms, Techniques and Active Vision; Part: Boston, MA, USA; Part: 16-18 Nov. 1992; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.1825; 1992; pp. 469-80 Abstract The medial axis skeleton is a thin line graph that preserves the topology of a simply connected region. The skeleton has often been cited as a useful representation for shape description, region interpretation, and object recognition. Unfortunately, the computation of the skeleton is extremely sensitive to variations in the bounding contour. Tiny perturbations in the contour often lead to spurious branches of the skeleton. In this paper, we consider a robust method for computing the medial axis skeleton across a variety of scales. The scale-space is parametric with the complexity of the bounding contour. The complexity is defined as the number of extrema of curvature in the contour. A set of curves is computed to represent the bounding contour across a variety of complexity measures. The curves possessing larger complexity measures represent greater detail than curves with smaller measures. A medial axis skeleton is computed directly from each contour. The result is a set of skeletons that represent only the gross structure of the region at coarse scales (low complexity), but represent more of the detail at fine scales (high complexity) Thesaurus computational complexity; image processing; image recognition Other Terms medial axis skeleton; multiple complexities; thin line graph; topology; simply connected region; shape description; region interpretation; object recognition; bounding contour variations; scale-space; bounding contour ClassCodes B6140C; C1250; C5260B Article Type Theoretical / Mathematical Coden PSISDG Language English RecordType Conference ControlNo. 4619983 AbstractNos. B9404-6140C-294; C9404-1250-184 ISSN 0277786X References 11 U.S. Copyright Clearance Center Code 0 8194 1026 8/92/$4.00 Country Pub. USA date 1207 ------------------------------------------------------------ Author Johansen, P.; Dept. of Comput. Sci., Copenhagen Univ., Denmark Title On the classification of toppoints in scale space Source Journal of Mathematical Imaging and Vision; J. Math. Imaging Vis. (Netherlands); vol.4, no.1; Jan. 1994; pp. 57-67 Abstract An algebraic classification scheme for toppoints in scale space is proposed. A critical point is a point whose spatial derivatives are zero, and a toppoint is a critical point in which the Hessian does not have full rank. A critical curve is a curve consisting of critical points. It is proposed that toppoints be classified according to the number of critical curves that intersect at the toppoint. Toppoints are analyzed further when one or two critical curves pass through the toppoint. Results on extrema, saddle points and intersections are found Thesaurus algebra; image recognition Other Terms toppoint classification; scale space; algebraic classification scheme; critical curves; extrema; saddle points; intersections ; image analysis ClassCodes B6140C; B0210; C1250; C1110 Article Type Theoretical / Mathematical Coden JIMVEC Language English RecordType Journal ControlNo. 4616880 AbstractNos. B9404-6140C-247; C9404-1250-149 ISSN 09249907 References 12 U.S. Copyright Clearance Center Code 0924-9907/94/$5.00 Country Pub. Netherlands date 1223 ------------------------------------------------------------ Author Kelch, J.; Wein, B.; Rogowski-Inst. for Electr. Eng., RWTH Aachen, Germany Title Model based segmentation of the tongue surface using a modified scale space filter Source Visual Communications and Image Processing '93; Part: Cambridge, MA, USA; Part: 8-11 Nov. 1993; Sponsored by: SPIE; Proceedings of the SPIE - The International Society for Optical Engineering; vol.2094, pt.1; 1993; pp. 24-30 Abstract This edge detector is based on coarse-to-fine tracking by varying the smoothing parameter of the Laplacian-of-Gaussian filter (LoG). In this way contour segments of the tongue dorsum and other objects are extracted. A model supports identification of the tongue segments and interpolation of the surface in the spatiotemporal space. The tongue is formed as a chain of elliptical structure elements. This model stresses a direction to detect the orientation of the tongue and is flexible enough to form any shape. These structure elements are matched to the scale segments by correlation. A trainable cost path classifier selects the topological connections of the structure elements, which are linked by a spline interpolation. Virtual three-dimensional views of the contour surface in the spatiotemporal space are generated with different azimuthal angles for visualization Thesaurus biomedical ultrasonics; correlation methods; edge detection; image segmentation; medical image processing; physiological models; spatial filters; splines (mathematics); topology; tracking Other Terms model-based segmentation; ultrasonic imaging; modified scale space filter; edge detector; coarse-to-fine tracking; tongue segments; elliptical structure elements; trainable cost path classifier; topological connections; spline interpolation; visualization ClassCodes A8760B; A4230V; A0260; B7510B; B7820; B6140C; B0290F; C7330 ; C5260B; C4130 Article Type Applications; Theoretical / Mathematical Coden PSISDG Language English RecordType Conference ControlNo. 4614536 AbstractNos. A9408-8760B-003; B9404-7510B-029; C9404-7330-074 ISSN 0277786X References 11 U.S. Copyright Clearance Center Code 0 8194 1369 0/93/$6.00 Country Pub. USA date 1220 ------------------------------------------------------------ ------------------------------------------------------------ Author Alvarez, L.; Guichard, F.; Lions, P.-L.; Morel, J.-M.; Dept. de Inf. y Sistemas, Las Palmas Univ., Spain Title Axioms and fundamental equations of image processing Source Archive for Rational Mechanics and Analysis; Arch. Ration. Mech. Anal. (Germany); vol.123, no.3; 1993; pp. 199-257 Abstract Image-processing transforms must satisfy a list of formal requirements. We discuss these requirements and classify them into three categories: 'architectural requirements' like locality, recursivity and causality in the scale space, 'stability requirements' like the comparison principle and 'morphological requirements', which correspond to shape-preserving properties (rotation invariance, scale invariance, etc.). A complete classification is given of all image multiscale transforms satisfying these requirements. This classification yields a characterization of all classical models and includes new ones, which all are partial differential equations. The new models we introduce have more invariance properties than all the previously known models and in particular have a projection invariance essential for shape recognition. Numerical experiments are presented and compared. The same method is applied to the multiscale analysis of movies. By introducing a property of Galilean invariance, we find a single multiscale morphological model for movie analysis Thesaurus image processing; partial differential equations; transforms Other Terms image processing; transforms; architectural requirements; locality; recursivity; causality; stability requirements; comparison principle; morphological requirements; shape- preserving properties; rotation invariance; scale invariance; image multiscale transforms; partial differential equations; movies; Galilean invariance; multiscale morphological model ClassCodes B6140C; B0230; B0220; C1250; C4170 Article Type Theoretical / Mathematical Coden AVRMAW Language English RecordType Journal ControlNo. 4603142 AbstractNos. B9404-6140C-001; C9404-1250-001 ISSN 00039527 References 82 Country Pub. Germany date 1210 ------------------------------------------------------------ Author Ottenberg, K.; Neumann, H.; Stiehl, H.S.; Philips GmbH Forschungslab., Hamburg, Germany Title Quantitative description and reconstruction of intensity functions using scale-space and multiresolution processing Source Signal Processing VI - Theories and Applications. Proceedings of EUSIPCO-92, Sixth European Signal Processing Conference; Part: Brussels, Belgium; Part: 24-27 Aug. 1992; Sponsored by: Belgian Nat. Fund for Sci. Res.; CERA; LMS Int; Amsterdam, Netherlands; Elsevier; 3 vol. lvii+1844; 1992; pp. 1425-8 vol. 3 Editor Vandewalle, J.; Boite, R.; Moonen, M.; Oosterlinck, A. Abstract Differently scaled 1-D intensity discontinuities along the gradient direction define significant image structure intrinsically related to different physical causes in the scene, e.g. step-edges in images due to cast shadow boundaries or gradually changing edge profiles due to self shadow boundaries on smoothly curved object surfaces. Reliable detection and quantitative description of such a variety of scaled intensity discontinuities (with a priori unknown relative contrasts, widths, loci and orientations) in images require a family of appropriately scaled operators and a generally applicable quantitative multiscale processing scheme. Hence the authors propose (i) a multiscale scheme following Marr's (1976) and Korn's (1988) approach (ii) briefly present the theoretical framework for the 1-D case, and (iii) briefly report on numerical results from initial experiments with discrete 1-D data Thesaurus image processing Other Terms scale-space processing; image structure description; intensity functions; multiresolution processing; 1-D intensity discontinuities; gradient direction; image structure; edge profiles; shadow boundaries; relative contrasts; widths; loci; orientations; scaled operators; multiscale processing ClassCodes B6140C Article Type Theoretical / Mathematical; Experimental Language English RecordType Conference ControlNo. 4584721 AbstractNos. B9403-6140C-084 ISBN or SBN 0 444 89587 6 References 14 Country Pub. Netherlands date 1204 ------------------------------------------------------------ Author Laine, A.; Fan, J.; Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA Title Texture classification by wavelet packet signatures Source IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Trans. Pattern Anal. Mach. Intell. (USA); vol.15, no.11; Nov. 1993; pp. 1186-91 Abstract This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (D/sub 20/) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (D/sub 6/) In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture Thesaurus feature extraction; feedforward neural nets; image recognition; wavelet transforms Other Terms texture classification; wavelet packet signatures; scale- independence; wavelet packet spaces; sensitivity; selectivity; energy metrics; entropy metrics; scale space representations; scale sensitivity; orientation sensitivity; two-layer network classifier ClassCodes B6140C; C1250; C1230D Article Type Theoretical / Mathematical Coden ITPIDJ Language English RecordType Journal ControlNo. 4580750 AbstractNos. B9403-6140C-039; C9403-1250-028 ISSN 01628828 References 40 U.S. Copyright Clearance Center Code 0162-8828/93/$03.00 Country Pub. USA date 1220 ------------------------------------------------------------ Author Eggert, D.W.; Bowyer, K.W.; Dyer, C.R.; Christensen, H.I.; Goldgof, D.B.; Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA Title The scale space aspect graph Source IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Trans. Pattern Anal. Mach. Intell. (USA); vol.15, no.11; Nov. 1993; pp. 1114-30 Abstract Currently the aspect graph is computed from the theoretical standpoint of perfect resolution in object shape, the viewpoint and the projected image. This means that the aspect graph may include details that an observer could never see in practice. Introducing the notion of scale into the aspect graph framework provides a mechanism for selecting a level of detail that is "large enough" to merit explicit representation. This effectively allows control over the number of nodes retained in the aspect graph. This paper introduces the concept of the scale space aspect graph, defines three different interpretations of the scale dimension, and presents a detailed example for a simple class of objects, with scale defined in terms of the spatial extent of features in the image Thesaurus graph theory; image processing Other Terms scale space aspect graph; projected image; scale dimension; image processing ClassCodes B6140C; B0250; C1250; C1160 Article Type Theoretical / Mathematical Coden ITPIDJ Language English RecordType Journal ControlNo. 4580745 AbstractNos. B9403-6140C-036; C9403-1250-025 ISSN 01628828 References 39 U.S. Copyright Clearance Center Code 0162-8828/93/$03.00 Country Pub. USA date 1220 ------------------------------------------------------------ Author Lindeberg, T.; Dept. of Numerical Anal. & Comput. Sci., R. Inst. of Technol., Stockholm, Sweden Title Effective scale: a natural unit for measuring scale-space lifetime Source IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Trans. Pattern Anal. Mach. Intell. (USA); vol.15, no.10; Oct. 1993; pp. 1068-74 Abstract A manner in which a notion of effective scale can be introduced in a formal way is developed. For continuous signals, a scaling argument directly gives a natural unit for measuring scale-space lifetime in terms of the logarithm of the ordinary scale parameter. That approach is, however, not appropriate for discrete signals since an infinite lifetime would be assigned to structures existing in the original signal. It is shown how such an effective scale parameter can be defined to give consistent results for both discrete and continuous signals. The treatment is based on the assumption that the probability that a local extremum disappears during a short-scale interval should not vary with scale. As a tool for the analysis, estimates are given of how the density of local extrema can be expected to vary with scale in the scale-space representation of different random noise signals both in the continuous and discrete cases Thesaurus image processing Other Terms effective scale; scale-space lifetime measurement unit ClassCodes B6140C; C1250 Article Type Theoretical / Mathematical Coden ITPIDJ Language English RecordType Journal ControlNo. 4580739 AbstractNos. B9403-6140C-031; C9403-1250-020 ISSN 01628828 References 20 U.S. Copyright Clearance Center Code 0162-8828/93/$03.00 Country Pub. USA date 1219 ------------------------------------------------------------ Author Neumann, H.; Ottenberg, K.; Hamburg Univ., Germany Title Estimating ramp-edge attributes from scale-space Source Signal Processing VI - Theories and Applications. Proceedings of EUSIPCO-92, Sixth European Signal Processing Conference; Part: Brussels, Belgium; Part: 24-27 Aug. 1992; Sponsored by: Belgian Nat. Fund for Sci. Res.; CERA; LMS Int; Amsterdam, Netherlands; Elsevier; 3 vol. lvii+1844; 1992; pp. 603-6 vol.1 Editor Vandewalle, J.; Boite, R.; Moonen, M.; Oosterlinck, A. Abstract Ramp-edges are frequently employed for modelling transitions of the image intensity function between regions of constant intensity value. For the domain of magnetic resonance images (MRI), this type of 'discontinuity' model can explicitly be derived from the underlying scene and the characteristics of the measurement process (e.g. partial-volume effects). For an automatic analysis of MRI, an estimation of the attributes defining such a ramp-edge is essential. Other models for the smooth image-transitions between plateaus corresponding to different anatomical structures, generated by partial-volume effects, within MRI have been investigated in detail by the authors (1989, 1992). These models are based on step-edge models convolved with Gaussian kernels, i.e. error-functions of different variance. An analysis of the accuracy of regularized first- and second-order differential operators for the localization of discontinuities (including ramp-edges) has been published by the authors (1990). The authors describe a procedure to estimate the remaining attributes of a ramp-edge, the transition width and the local contrast, from the scale-space representation of the ramp-edge intensity function, generated by the Gaussian convolution kernels Thesaurus biomedical NMR; edge detection; medical image processing Other Terms ramp-edge attributes estimation; image intensity function; constant intensity value; magnetic resonance images; partial- volume effects; MRI; step-edge models; Gaussian kernels; error-functions; second-order differential operators; transition width; local contrast; scale-space representation ClassCodes A8760G; A8770E; B7510B; B6140C Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4576307 AbstractNos. A9404-8760G-029; B9402-7510B-137 ISBN or SBN 0 444 89587 6 References 0 Country Pub. Netherlands date 1204 ------------------------------------------------------------ Author Wong, Y.-F.; Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA Title Clustering data in the scale space Source ICARCV '92. Second International Conference on Automation, Robotics and Computer Vision; Part: Singapore; Part: 16-18 Sept. 1992; Sponsored by: IEE; Inst. Meas.& Control; Econom. Development Board; et al; Singapore; Nanyang Technol. Univ; 3 vol. (viii+934+viii+861+vii+908); 1992; pp. CV-20.2/1-5 vol.1 Abstract We derive a scale-space clustering algorithm based on information theory and statistical mechanics. We introduce the concept of cluster independence into clustering. The cluster centers correspond to the local minima of a thermodynamical free energy, which can be analyzed using nonlinear dynamics. The algorithm works by melting the system to produce a tree of clusters in the scale space. Melting can also handle variability in cluster densities, cluster sizes and ellipsoidal shapes. We tested successfully the algorithm on both simulated data and real data Thesaurus dynamics; information theory; pattern recognition; statistical mechanics Other Terms data analysis; scale-space clustering algorithm; information theory; statistical mechanics; local minima; thermodynamical free energy; nonlinear dynamics ClassCodes B6140C; B0240Z; B6110; C1250; C1140Z; C1260 Article Type Theoretical / Mathematical Language English RecordType Conference ControlNo. 4575742 AbstractNos. B9402-6140C-283; C9402-1250-217 References 19 Country Pub. Singapore date 1205 ------------------------------------------------------------ Author Stach, J.; Shaw, S.; SRI Int., Menlo Park, CA, USA Title Robust relational trees by scale-space filtering Source Proceedings of the IEEE-SP International Symposium Time-Frequency and Time-Scale Analysis (Cat.No.92TH0478-8); Part: Victoria, BC, Canada; Part: 4-6 Oct. 1992; Sponsored by: IEEE; New York, NY, USA; IEEE; 577; 1992; pp. 173-6 Abstract A relational tree (RT) is a signal representation used to discriminate between multidimension