Invariant descriptions and associative processing applied to object recognition under occlusions

Abstract

Object recognition under occlusions is an important problem in computer vision, not yet completely solved. In this note we describe a simple but effective technique for the recognition objects under occlusions. The proposal uses the most distinctive parts of the objects for their further detection. During training, the proposal, first detects the distinctive parts of each object. For each of these parts an invariant description in terms of invariants features is next computed. With these invariant descriptions a specially designed set of associative memories (AMs) is trained. During object detection, the proposal, first looks for the important parts of the objects by means of the already trained AM. The proposal is tested with a bank of images of real objects and compared with other similar reported techniques. © Springer-Verlag Berlin Heidelberg 2003.