Stereo using Monocular Cues within the Tensor Voting Framework
Abstract
We address the fundamental problem of matching two static images.
Significant progress has been made in this area, but the
correspondence problem has not been solved. Most of the remaining
difficulties are caused by occlusion and lack of texture. We
propose an approach that addresses these difficulties within a
perceptual organization framework, taking into account both
binocular and monocular sources of information. Geometric and
color information from the scene is used fog grouping,
complementing each other's strengths. We begin by generating
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 projections of which on the images serve as the
reliable set of matches. The use of segmentation based on
geometric cues to infer the color distributions 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
unmatched 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.