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Stereo Vision: Two Alternate Approaches for the Matching Problem

Stereo imaging, a branch of artificial vision, is used to determine objects' locations in three dimensions. The primary computational problem in stereo imaging is the identification of corresponding locations in two images, i.e. the matching problem. Traditionally, matching methods have been of three types: brute force (determining intensity or edge correlation between "sliding windows"), shape based (connectivity analysis yielding geometric features), or region segmentation based on classifiers (texture). We present initial results from the development of two alternate methods of image matching: color and varying camera baseline separation. While monochrome stereopsis must depend primarily on determination and matching of geometric features, color provides an added dimension that can either support geometric feature matching or alleviate stringent geometric matching criteria. Color features, extracted by use of varying color filters and signal separators, are used in the determination of corresponding objects between two images. In addition, in conventional stereo vision systems, the baseline separation of the two cameras is fixed, yielding a compromise between the resolution achievable and the ease with which features can be matched. A varying baseline strategy preserves the correspondence of paired features in each image, while allowing an increase in resolution.