Abstract

We address the fundamental problem of matching two static images. Significant progress has been made in this area, but the cor- respondence problem has not been solved. Most of the remaining difi- culties are caused by occlusion and lack of texture. We propose an ap- proach that addresses these dificulties within a perceptual organization framework, taking into account both binocular and monocular sources of information. Geometric and color information from the scene is used for grouping, complementing each other’s strengths. We begin by gener- ating matching hypotheses for every pixel in such a way that a variety of matching techniques can be integrated, thus allowing us to combine their particular advantages. Correct matches are detected based on the support they receive from their neighboring candidate matches in 3-D, after tensor voting. They are grouped into smooth surfaces, the pro jec- tions of which on the images serve as the reliable set of matches. The use of segmentation based on geometric cues to infer the color distribu- tions of scene surfaces is arguably the most significant contribution of our research. The inferred reliable set of matches guides the generation of disparity hypotheses for the unmatched pixels. The match for an un- matched pixel is selected among a set of candidates as the one that is a good continuation of the surface, and also compatible with the observed color distribution of the surface in both images. Thus, information is propagated from more to less reliable pixels considering both geometric and color information. We present results on standard stereo pairs.