Genetic Algorithms for Visual Reconstruction Problems

9210648 Vemuri所提出的研究的目标是开发一个统一的计算框架的几个低层次的视觉问题属于通用的描述。 这些问题的大多数文献中的配方导致非凸泛函的最小化。 现有的最小化技术（随机或确定性）要么计算缓慢，或只有在某些限制性的假设是有效的。 因此，有一个关键的需要检查替代的优化技术，不容易受到现有技术的陷阱，拟议的研究是在这个方向上的尝试。 本研究关注的是应用一种相对较新的技术，称为遗传算法（GAs）的各种视觉重建问题，即立体匹配，不连续保持表面重建，和结构形式的运动。 拟议的研究将集中在使用马尔可夫链的GA的分析建模，以促进算法的收敛性分析时，应用到视觉重建问题所涉及的问题。 理论工作将与算法实现和测试的真实的图像数据。 所提出的统一计算框架将显著推进计算视觉的最新发展。 ***

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. ***