This research continues earlier efforts of the PI in studying parallelism for image understanding and robotics as well as in understanding the power of reconfiguration. There are three spheres of emphasis in this work: 1. design and analysis of efficient parallel algorithms for roblems in vision and robotics on well-established parallel models of computation, 2. implementation of the parallel solutions on state of the art parallel machines, and 3. a study of the power of reconfigurable meshes in solving fundamental problems of interest to the parallel processing community as well as problems arising in image processing, vision and robotics. These problems are among the generic high and intermediate level problems in image understanding and robotics. Specifically, in image understanding, design and analysis of algorithms for motion analysis (object tracking), image and stereo matching, model based object recognition as well as algorithms for several symbolic computation based approaches used in understanding images will be investigated. In robotic applications, parallel solutions to a variety of practical problems in real time robot motion- and task-planning arising in terrain navigation and industrial automation will be investigated. The parallel models to be employed include the mesh-connected processor array, reconfigurable mesh array, and the hypercube. The algorithms will be implemented on Connection Machine CM-5, Maspar MP-1 and the Image Understanding Architecture (IUA). In the work on the reconfigurable mesh model, design and analysis of fast and processor efficient parallel solutions to several fundamental problems on the reconfigurable mesh will be investigated. Problems to be considered include arithmetic problems, image problems and geometric problems on planar points. Known techniques on other parallel models will be studied for possible mapping onto the reconfigurable mesh.
