Journal of Pattern Recognition Research

This paper addresses the problem of generic object recognition from arbitrary viewpoints by modeling the perceptual capability of human beings. A framework is proposed which uses a combination of 3D appearance and 2D shape cues to recognize the object class as well as determine its pose. The hierarchical framework combines two stages. First, the 3D appearance model of the object is captured from multiple viewpoints using linear subspace analysis techniques to reduce the search space. A decision-fusion based combination of 2D PCA and ICA is used to integrate the complementary information of classifiers and improve appearance-based recognition accuracy. Shape matching is then performed on the reduced search space. The proposed framework uses a decision fusion technique, in which evidences from 3D appearance and 2D shape are combined (fused) to obtain the correct object class and its pose. Results are presented on objects with complex appearance and shape characteristics from the COIL-100 and IGOIL (IITM Generic Object Image Library) databases. IGOIL database is also used to analyze the appearance manifolds along two orthogonal axis of rotation. Performance degradation in case of noisy images is also presented.