SGER: MCMC Algorithms for Object Recognition

SGER：用于对象识别的 MCMC 算法

• 批准号: 9979201
• 负责人: David Forsyth
• 金额: $ 5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9979201&HistoricalAwards=false
• 关键词: SGER; MCMC; Algorithms; Object; Recognition

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.

摘要IIS-9979201 Hollerbach，约翰犹他大学$50，000 - 12个月。MCMC算法的对象配置这是一个标准的SGER奖。 这项探索性的研究是关于通过概率和几何技术的混合在图像中找到穿着衣服的人。目前的物体识别理论不能用于识别这种复杂的可变形物体，特别是因为它们不允许精确的几何模型，并且在杂乱的背景上。 在这项研究中，基于学习的自下而上的方法将被用来组装图像基元（由不变滤波器定义）到可能是衣服的区域，然后将这些区域组装到可能是身体部位的区域，这些部位将进一步组装成四肢，等等，直到一个区域足够大，表明一个人存在。这种纯粹自下而上的方法将辅之以马尔可夫链蒙特卡罗（MCMC）技术，以修改现有的群体。目的是获得具有高后验概率的情况。特别地，如果产生的组具有高后验概率，则没有通过组装测试的片段集合将被随机地通过这些测试。