Computer Vision: Algorithms and Architectures

Ever since computers were used for pattern recognition, image processing, and more generally for vision, a number of special-purpose algorithms and architectures have been developed. As new architectures reached the construction stage, different classes of algorithms emerged in order to produce more effective and efficient solutions to the new, heavy burdens of color and moving images, stereo vision, and real-time performance. The multiprocessor machines (including a variety of interconnection patterns, memory organization, control structures, and input-output management) stimulated algorithm designers to develop suitable data structures, choice of appropriate primitive operations, adequate sequencing of input image data, use of concurrency of local computations, etc. This chapter presents a review of some basic image-processing algorithms implemented on different multiprocessor machines. Algorithms for connected component labeling, line detection, and stereo matching are considered on the systolic, mesh, tree, and pyramid machines. The analysis and evaluation of these algorithms may hopefully lead to their use in real applications in a cost-effective way.