Line Feature-based Object Recognition for Textureless Indoor Objects
People^Top
Gunhee Kim
Martial Hebert
Sung-Kee Park
Description^Top
Our approach relies on 1) segmented linear features, and 2) pairwise
geometric relationships between features. This approach is motivated by
the need for recognition strategies that can handle many of indoor
objects that have no little or not textural information on their
surfaces, but have strong geometrical consistency within the same
object class.
Our matching method consists of two steps. First, we find
correspondence candidates between linear fragments. Second, a spectral
matching algorithm is used to find the subset of correspondences which
is the most consistent. Both matching methods are learnt by using
logistic classifiers.
We evaluated the developed recognition system with our own database,
which is composed of eight indoor object classes. We also compared the
performance of our line feature based recognition approach with a SIFT
feature based method. Experimentally, it turned out that the line
features are superior in our problem setup - the detection of
textureless objects.
Our dataset consists of eight object classes, which are
refrigerators, microwave, ovens, monitors, phones, printers, sofas,
wall clocks, and wiry chairs. The images are taken in kitchens and
hallways at CMU. The database is composed of 40 labeled training images
for eight object classes (i.e., five images each object class), and 145
test images. Our recognition system is robust in the variation of
object instances and illumination to some extent (Fig.2(a)(c)). It is
generally successful for scenes with clutter and with only partial view
of the target object (Fig.13(b)(d)). Fig.3 shows what causes the
failures of detection of monitors. Apparently, monitors are quite
similar to other objects which have rectangular shapes, especially
microwave ovens. This kind of confusions occurs with other pairs of
objects such as sofas/wiry chairs, and fridge/printers. Another
explanation is that monitors are generally located on the table and
other objects on the desktop could distract the system. Fig.3(a) is a
good example of this.
Figure 1. A dataset - 8 object classes
(a) Intra-class variation (b) In the
cluttered scene (c) Illumination variation (d) With only partial view
Figure 2. Examples of correct matching.
Figure 3. Examples of confusion -
Similarly looking objects.
Publication^Top
- Gunhee Kim, Martial Hebert, and Sung-Kee Park, "Preliminary Development of a Line Feature-based Object Recognition System for Textureless Indoor Objects", in Springer-LNCIS Publication "Recent Progress in Robotics: Viable Robotic Service to Human." S. Lee, I. Hong Suh, M. Sang Kim (eds.) (Selected papers from the 2007 International Conference on Advanced Robotics (ICAR 2007)), 2007.
Funding^Top
- Intelligent Robotics Development Program, a 21st Century Frontier R&D Programs by the Ministry of Commerce, Industry, and Energy of Korea.
Copyright notice^Top
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