Year 2003 (in chronological order)
Date  Presenter  Description 
1/29/2003
Wednesday 
Shyjan Mahamud 
For our next meeting, I will be talking about applications of
loopy belief propagation in computer vision.
First, good tutorials on belief propagation (BP) can be found: here. In particular the tech. report. I will then review 3 applications of BP that I have found: "Recovering Shading and Reflectance from a Single Image", M. Tappen, W.T. Freeman, E.H. Adelson, NIPS 2002. Paper here. "Finding Deformable Shapes Using Loopy Belief Propagation", J.M. Coughlan, S.J. Ferreira, ECCV 2002. Paper here. "Enforcing Integrability for Surface Reconstruction Algorithms Using Belief Propagation in Graphical Models", N. Petrovic, I. Cohen, B. Frey, R. Koetter, T.S. Huang, CVPR 2001. Paper here. I may not be able to go into much detail on all of these applications in one talk as I will be focusing more on reviewing the theory first. However, if there is enough interest, we can talk about these papers in more detail in another meeting. 
2/5/2003
Wednesday 
Martial Hebert 
I will talk about some topics on closest point
search and approximate NN recently making its way into C.V. applications.
Related Papers: P. Indyk and R. Motwani, "Approximate nearest neighbor  towards removing the curse of dimensionality", In Proceedings of the 30th Symposium on Theory of Computing, 1998. Paper. Recognizing 3D Objects from Range Data. Andrea Frome, Thomas Bulow, and Jitendra Malik. Abstract. (Submitted to CVPR 2003.) Additional info: 
2/12/2003
Wednesday 
Daniel Huber 
Here is the paper that we will be reading:
Torr and Zisserman, MLESAC: A new robust estimator with application to estimating image geometry, CVIU 78:1, April 2000, p. 13856. Paper here. Zhengyou Zhang at INRIA has a nice tutorial on parameter estimation in HTML and Postscript. 
2/19/2003
Wednesday 
Pragyana Mishra 
Here is the abstract of the talk I will be giving at
this week's reading group. Since it is regarding my
recent work, I do not have a paper for your
perusal.
A Simple Approach to Alignment without Correspondence This talk will present a new and generalized framework to to align a model with respect to an image. The approach does not explicitly require the nature of imaging or model registration process, illumination and reflectance conditions, surface properties of the scene, determining occlusions and correspondence of features between data from sensors. The method is based on a new formulation of minimizing a distance measure relating the statistics of two randomly sampled data sets, in our case, a range model and an intensity image. Sensor data have regions best suitable for computing a statistic while parts of it are cluttered or unresolved. The framework exploits the General Crofton Theorem in integral geometry to combine statistics from disparate local regions of the data. This new approach can accommodate data from different sensors and can be used to align data of same or varied modalities. 
2/26/2003
Wednesday 
Fernando De La Torre 
Next week I will present a couple of papers (the second one just if I have enough time).
Both papers are related to constructing generative models from image sequences.

3/5/2003

No Meeting.  
3/12/2003

No Meeting.  
3/19/2003
Wednesday 
Caroline Pantofaru  Dorin Comaniciu. An Alogorithm for DataDriven Bandwidth Selection. PAMI, vol. 2, no. 2, pp. 281288, February 2003. Paper here. 
3/26/2003
Wednesday 
Ranjith Unnikrishnan  "Robust Computer Vision through Kernel Density Estimation", Haifeng Chen and Peter Meer, ECCV 02. Paper here. 
4/2/2003
Wednesday 
Derek Hoiem 
FloatBoost is similar to Adaboost except that instead of merely adding weak
classifiers to the strong classifier, weak classifiers may be removed from
the classifier as well. The intent is to overcome some of the greediness
of the Adaboost method and to find a smaller set of weak classifiers that
achieves equal or greater performance. The FloatBoost paper introduces
FloatBoost, a more flexible feature set than ViolaJones, and a hierarchical
detector for multiview detection of faces:
S.Z. Li, L. Zhu, Z.Q. Zhang, A. Blake, H.J. Zhang, H. Shum. "Statistical Learning of MultiView Face Detection". In Proceedings of The 7th European Conference on Computer Vision. Copenhagen, Denmark. May, 2002. Paper here and here. 
4/9/2003
Wednesday 
Chuck Rosenberg 
I am going to discuss the object detection work of David Lowe using
local scaleinvariant features. We've mentioned this work a number of
times but we've never actually read the papers in the group. Martial
has already presented much of the background material in his talk
about a year ago on 7/10/2002.
The basic paper is: Object recognition from local scaleinvariant features. David G. Lowe, International Conference on Computer Vision, Corfu, Greece (September 1999), pp. 11501157. Paper here. This paper extends the concept to better handle variations in 3D pose: Local feature view clustering for 3D object recognition, David G. Lowe, IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii (December 2001), pp. 682688. Paper here. 
4/16/2003
Wednesday 
Owen Carmichael 
For the next misc reading group I'll be talking about the superresolution
of image sequences, i.e. inferring a highresolution image from a sequence
of overlapping lowresolution images. (This is in contrast to the
singleimage superresolution problem, where the goal is to infer a
highresolution image from a single low resolution image plus prior
knowledge or training data).
The reference is: Michael E Tipping and Christopher M Bishop. Bayesian Image Superresolution. Proceedings NIPS 2002. Paper here. I will discuss how this relates to earlier work along the same line by Peter Cheeseman, Blake and Zisserman, and others, time permitting. 
4/23/2003
Wednesday 
Anuj Kapuria 
I have selected a paper, from PAMI, August 2002 (Vol. 24, No. 8),
pp. 10751090. The title is:
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction. Wai Lam, ChiKin Keung and Danyu Liu. Paper here. 
4/30/03
Wednesday 
Sanjiv Kumar  I will be talking about the work of Torralba, Murphy, Billman and Rubin on the use of context for object detection/recognition: 
5/7/03
Wednesday 
Edward H. Adelson 
Special RI Seminar
On Seeing Stuff: The Perception of Materials by Humans and Machines 
5/14/03
Wednesday 
David Tolliver  Kernel Principal Angles for Classification Machines with Applications to Image Sequence Interpretation. Lior Wolf and Amnon Shashua. CVPR 2003. Paper here. 
5/21/03
Wednesday 
Goksel Dedeoglu 
This Wednesday I would like to talk about the following paper:
Storkey A.J., "Dynamic structure superresolution", NIPS 2002. Paper here. Comparison with other techniques is available here. 
5/28/2003

No Meeting.  
6/4/2003
Wednesday 
Bart Nabbe 
"An Efficient Solution to the FivePoint Relative Pose Problem",
David Nister, Sarnoff Corporation, CVPR 2003.

6/11/2003
Wednesday 
Raghu Rao 
I shall be talking about a variant of NN classifier. The two papers are:
Stan Z Li, J.Lu, 'Face Recognition Using the Nearest Feature Line Method', IEEE Trans on Neural Networks, vol.10, no.2, pp.439443, Mar.1999. Paper here. and J.T. Chien, CC, Wu, 'Discriminant Waveletfaces and nearest feature classifiers for face recognition', IEEE Trans. PAMI, 24(12):16441649. 2002. Paper here. In the first paper, they introduce, Nearest_Feature_Line (NFL) classifier a variant of Nearest Neighbor Classifier (NN), in which any two feature points of the same class (person's face) are generalized by a feature line (FL) passing through the two points. They found that this performs better than simple NN classifier. In the second paper, they extend this idea to Nearest_feature_plane (NFP) and Nearest_feature_space (NFS). NFP considers sets of three features in the class, while NFS considers subspaces of any dimension. Turns out NFP/NFS do better than the others (NFL and NN). 
6/25/2003
Wednesday 
Everyone 
CVPR 2003 Conference Sweep
The goal of this meeting is to compile a list of papers from CVPR that we would like to read and understand in more detail in coming meetings and have a short discussion about the overall trends at CVPR. 
7/2/2003
Wednesday 
Cristian Dima  The workshop is called "Multiple Classifier Systems", and this year's edition of the program is here. I will go over the papers I have found interesting and if (my) time permits I will also try to go over the key points in the invited talk by Friedman. 
7/9/2003
Wednesday 
Shyjan Mahamud 
For the next group meeting,
I will be giving a talk on Helmholtz Stereopsis for the uncalibrated case:
"Towards a Stratification of Helmholtz Stereopsis", T. Zickler, P. Belhumeur, D. Kriegman, CVPR 2003. Paper here. I will also review related work at the same conference: "Surface Reconstruction via Helmholtz Reciprocity with a Single Image Pair",P. Tu, P. Mendonca, CVPR 2003. 
7/16/2003
Wednesday 
Daniel Huber 
For next week's reading group, I'll discuss:
Object Class Recognition by Unsupervised ScaleInvariant Learning by Fergus, Perona, and Zisserman. CVPR 2003. Paper here. Hopefully, it will give us some ideas for our paper on scaleinvariant betaboosting. ;) 
7/23/2003
Wednesday 
Fernando De La Torre 
Next week I will try to understand (no guarantees are given) the following
paper:
Optimal Linear Representations of Images for Object Recognition X. Liu, A. Srivastava, K. Gallivan (Florida State University) (CVPR 2003) Paper here. In case I have some extra time, you some interest and it is before 3 p.m I will also try to go through a related paper: Statistical Search for Hierarchical Linear Optimal Representations of Images Q. Zhang, X. Liue, A. Srivastava. (IEEE CVPR 2003 Workshop on Statistical Analysis in Computer Vision) Paper here. 
7/30/2003
Wednesday 
Pragyana Mishra 
I will present the paper on
VectorValued Image Regularization with PDE's: A Common Framework for Different Applications. by D. Tschumperle and R. Deriche. Paper here. The paper got the 'best student paper award' at CVPR03. 
8/6/2003

No meeting because of the VASC Retreat.  
8/13/2003
Wednesday 
Ranjith Unnikrishnan 
I'll be introducing the paper
"Practical Nonparametric Density Estimation on a Transformation Group
for Vision", E. Miller, C. Chefd'hotel (CVPR '03)
(paper here) that talks about
estimating densities of linear shape deformations for the purpose of OCR.
For motivation / background on the classifier model they use for some of their results, read "Learning from One Example Through Shared Densities on Transforms.", E.Miller, N.Matsakis, P.Viola, (CVPR '00), (paper here). I'll try and cover both papers, time and sanity permitting. 
8/20/2003
Wednesday 
Caroline Pantofaru 
I'm going to discuss:
Edge Detection with Embedded Confidence. Peter Meer, Bogdan Georgescu. PAMI, vol. 23, no. 12, December 2001. Paper here. 
8/27/2003
Wednesday 
Derek Hoiem 
I'll present "KullbackLeibler Boosting" (CVPR 2003) by Liu and Shum from Microsoft on
Wednesday (Sanjiv said he will be covering different papers). If there is
time, I may also cover "An efficient approach to learning inhomogeneous
Gibbs model" (CVPR 2003) by Liu, Chen, and Shum. The first paper describes an approach
to feature generation and selection and classification using the
KLdivergence metric and boosting. The second paper describes a method for
fast parameter learning of Inhomogeneous Gibbs model in high dimensional
spaces. The first paper also describes an application in face detection,
while the second describes an application in the allimportant domain of
caricature generation.
The papers are available online: Paper 1 (local copy) and Paper 2 (local copy). 
9/3/2003  No Meeting  
9/10/2003
Wednesday 
Chuck Rosenberg 
I'll be presenting:
"A Bayesian Approach to Unsupervised Oneshot Learning of Object Categories" by Li FeiFei, Rob Fergus, Pietro Perona. Pages 11341141. ICCV 2003. Paper here. 
9/17/2003
Wednesday 
David Tolliver 
I'll present Stella Yu's paper on KWay Cuts:
Multiclass Spectral Clustering. Stella Yu and Jianbo Shi. Paper here. 
9/24/2003
Wednesday 
Sanjiv Kumar 
I will speak on nonparametric belief
propagation to do inference over arbitrarily connected graphs with
continuous latent variables:
Nonparametric Belief Propagation. E. Sudderth, A. Ihler, W. Freeman, and A. Willsky. CVPR, June 2003. Paper here. 
10/1/2003

No Meeting. 
10/8/2003

Owen Carmichael 
The reading group paper will be:
Forbes, F., Peyrard, N. Hidden markov random field model selection criteria based on mean fieldlike approximations. PAMI 9/2003. pg. 1089 1101. Paper here. The problem setting is using MRFs to solve "lowlevel" vision problems, i.e. estimating a property at each patch/pixel in an image based on image data and the properties of surrounding patch/pixels. The specific problem the paper addresses is how to select statistical models for the observation and compatibility functions in MRFs. In particular, the point is that standard model selection criteria like BIC scores are difficult to compute directly for MRFs, but you can approximate these criteria. 
10/15/2003
Wednesday 
Goksel Dedeoglu  I will be presenting my ongoing work on superresolving video sequences through hallucination. After a review of Baker and Kanade's face hallucination, I will propose a graphical model that can justify the energy function minimized by this algorithm. Subsequently, I will extend this framework into time, and show the regularizing role of temporal models in obtaining 16fold superresolved videos of a 6x8 pixelwide talking face. 
10/22/2003

No Meeting. 
10/29/2003
Wednesday 
Anuj Kapuria 
The paper I'll be talking about is:
Robust Data Clustering by
Ana L.N. Fred and Anil K.Jain.
CVPR 2003.
Paper here.
It describes a information theoretic appoach to the problem of robust clustering by combining data partitions produced by multiple clusterings. 
11/05/2003

No meeting. 
11/12/2003

No meeting. 
11/19/2003
Wednesday 
Cristian Dima  I am going to review the "Learning from Imbalanced Data Sets II" workshop from ICML 2003. There are no particular paper people should read, but in case they would like to have a look at them they can be found here. 
11/26/2003

No meeting. (Thanksgiving) 
12/03/2003
Wednesday 
Shyjan Mahamud 
I will go over the following ICCV 2003 paper:
"Image Parsing: Unifying Segmentation, Detection and Recognition", Z. Tu, X. Chen, A.L. Yuille, SC. Zhu. Paper here. I will try and analyze their work using Occam's Razor at least for the particular application they have in mind. 
Last modified: Mon Apr 29 2004