A methodology for transforming the original search space into an equivalent but minimal search space is proposed. First, the concept of dependences leads to a procedure for reduction of the search space. The search procedure using this method can produce a minimal and complete search space. It is shown that this method is applicable to parallel search as well. An added advantage of this method is that it does not exclude the use of heuristics. pi - lambda transformation is introduced to reduce the parallel search space.>

The problems in computer vision range from edge detection and segmentation at the lowest level to the problem of cognition at the highest level. This correspondence describes the organization and operation of a semantic network array processor (SNAP) as applicable to high level computer vision problems. The architecture consists of an array of identical cells each containing a content addressable memory, microprogram control, and a communication unit. The applications discussed in this correspondence are the two general techniques, discrete relaxation and dynamic programming. While the discrete relaxation is discussed with reference to scene labeling and edge interpretation, the dynamic programming is tuned for stereo.