Computing visual feature correspondences

[ahmad@interval.com]

The following paper is available for anonymous ftp on
archive.cis.ohio-state.edu (128.146.8.52), in directory
pub/neuroprose, as file "ahmad.correspondence.ps.Z":

Feature Densities are Required for Computing Feature Correspondences

Subutai Ahmad
Interval Research Corporation
1801-C Page Mill Road, Palo Alto, CA 94304
E-mail: ahmad at interval.com

			       Abstract

The feature correspondence problem is a classic hurdle in visual
object-recognition concerned with determining the correct mapping
between the features measured from the image and the features expected
by the model.  In this paper we show that determining good
correspondences requires information about the joint probability
density over the image features.  We propose "likelihood based
correspondence matching" as a general principle for selecting optimal
correspondences. The approach is applicable to non-rigid models,
allows nonlinear perspective transformations, and can optimally deal
with occlusions and missing features. Experiments with rigid and
non-rigid 3D hand gesture recognition support the theory. The
likelihood based techniques show almost no decrease in classification
performance when compared to performance with perfect correspondence
knowledge.


To appear in:
 
Cowan, J.D., Tesauro, G., and Alspector, J.  (Eds.), Advances in
Neural Information Processing Systems 6. San Francisco CA: Morgan
Kaufmann, 1994.