MERCREDI 27 JUIN, 17H30, AMPHI RATAUD

Using Shape Information for Recognition

Martial Hebert (hebert -at- ri.cmu.edu)
Robotics Institute
Carnegie-Mellon University

http://www.ri.cmu.edu/people/hebert_martial.html


Abstract: Shape information is an important cue for recognizing object
and object categories in images. In fact, many categories are
characterized primarily by the consistency of their shape while
intra-class texture statistics may not be as informative. This is true
even for categories that include a large degree of geometric
deformation.  Recent work in the community has shown progress in using
shape cues for recognition, including learned boundary detectors,
matching and classification using local configuration of contour
fragments. In this talk, I will review three recent developments in
this area. The first one is an algorithm for category recognition
which relies on very simple shape features (oriented points sampled on
contour fragments). The algorithm uses an efficient spectral matching
technique for both matching and learning. The category models can be
learned from semi-supervised data (i.e., images labeled as
containing/not containing the object without manual delination of the
object). An added benefit of this approach is that it uses an explicit
matching approach between image features and model parts. As a result,
it is possible to extend the classification algorithm to an efficient
detection algorithm, which includes object localization.  Two other
developments will be very briefly described. The first one has to do
with using motion information to detect boundaries; the second one
addresses the problem of extracting boundaries from a single image by
using estimates of the local geometry of the scene (using the results
from our earlier work on estimating geometric layout from an image).
Both approaches provide information about object boundaries that are
useful for recognition.