Parallel Processing has been widely used in image processing. A number of parallel architectures such as cellular arrays, memory augmented arrays, and pyramids have been proposed for these tasks. In the past, many problems in low- and medium- level vision have been solved on these architectures. This research will explore parallel architectures for a range of problems in computer vision and develop efficient parallel algorithms for them. As part of the research, several novel VLSI arrays including arrays with efficient global communication features, arrays with reduced processing requirements, as well as reconfigurable VLSI arrays will be developed. While these architectures are also suitable for many other problems, they seem to be particularly well-suited for vision applications due to inherent properties of problems in low-level vision. This research will also investigate parallel algorithms for medium-and high-level vision problems. Finally, the use of information theoretic techniques to study the interprocessor communication requirements and inherent parallel complexity of solving several fundamental problems in vision will be investigated.
