Genetic Algorithms for Visual Reconstruction Problems

[210648] Vemuri提出的研究目标是为几种属于通用描述的低级视觉问题开发一个统一的计算框架。文献中大多数这些问题的公式导致非凸泛函的最小化。现有的最小化技术（随机的或确定性的）要么计算缓慢，要么仅在某些限制性假设下有效。因此，迫切需要检查替代优化技术，这些技术不容易受到现有技术的陷阱的影响，而拟议的研究就是在这个方向上的尝试。本研究关注的是一种相对较新的称为遗传算法（GAs）的技术在各种视觉重建问题中的应用，即立体匹配、保持不连续的表面重建和结构形式运动。提出的研究将集中在使用马尔可夫链对遗传算法进行分析建模的问题上，以促进算法在应用于视觉重建问题时的收敛分析。理论工作将通过算法实现和实际图像数据的测试来完成。所提出的统一计算框架将极大地推动当前计算视觉领域的发展。* * *

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. ***