SGER: MCMC Algorithms for Object Recognition

摘要IIS-9979201霍尔巴赫，美国犹他州大学，$50,000-12。用于对象识别的MCMC算法这是一个标准的SGER奖。这项探索性的研究是关于通过混合使用概率和几何技术在图像中找到穿衣服的人。当前的目标识别理论不能用于识别如此复杂的可变形目标，特别是因为它们不允许精确的几何模型，并且处于杂乱的背景中。在这项研究中，将使用一种基于学习的自下而上的方法将图像基元(由不变过滤器定义)组装成可能是衣服的区域，然后将这些区域组装成可能是身体部分的区域，然后进一步组装成肢体，依此类推，直到一个区域足够大，以指示一个人的存在。这种纯粹自下而上的方法将得到马尔可夫链蒙特卡罗(MCMC)技术的补充，以修改现有的组。其目的是获得具有高后验概率的情况。特别是，如果获得结果的组具有很高的后验概率，则不能自行通过组装测试的片段集合无论如何都将随机通过这些测试。

Abstract IIS-9979201Hollerbach, JohnUniversity of Utah$50,000 - 12 mos.MCMC Algorithms for Object RecognitionThis is a standard SGER award. This exploratory research is about finding clothed people in images with a mixture of probabilistic and geometric techniques. Current theories of object recognition cannot be used to recognize such complex deformable objects, particularly because they do not allow exact geometric models and are on cluttered backgrounds. In this study, a bottom-up approach based on learning will be used to assemble image primitives (defined by invariant filters) into regions that could be clothing, and then to assemble these regions into regions that could be body segments, which will be assembled further into limbs, and so on, until a region is sufficiently large to indicate that a person is present. This purely bottom-up approach will be complemented by Markov chain Monte Carlo (MCMC) techniques to modify existing groups. The intention is to obtain cases that have high posterior probability. In particular, collections of segments that do not on their own pass assembly tests will be randomly advanced past those tests anyway, if the group that results has high posterior probability.