9210648 Vemuri The goal of the proposed research is to develop a unified computational framework for several low-level vision problems which fall under the generic descriptions. Formulations in literature of a majority of these problems lead to minimization of non-convex functionals. Existing minimization techniques (stochastic or deterministic) are either computationally tardy or are efficient only under certain restrictive assumptions. Hence, there is a critical need to examine alternate optimization techniques that are not susceptible to pitfalls of the existing techniques, and the proposed research is an attempt in this direction. This research is concerned with the application of a relatively new technique called genetic algorithms (GAs) to a variety of visual reconstruction problems namely, stereo matching, discontinuity preserving surface reconstruction, and structure form motions. The proposed research will focus on issues involved in the analytical modeling of the GA using Markov chains to facilitate convergence analysis of the algorithm when applied to Visual Reconstruction problems. The theoretical work will be concluded with algorithm implementation and testing on real image data. The proposed unified computational framework will significantly advance the state of the art in computational vision. ***
