Probabilistic Models for Image and Video Parsing

计算机视觉领域一个长期存在的问题是，如何将图像或视频解析成与场景中的单个物体或至少是物体的一部分相对应的块。例如，1983年，两位计算机视觉研究人员A. Witkin和J. Tenenbaum解释了人类似乎是如何将一个组织强加给视觉场景（图像或视频）的，从而押注于图像的哪些部分与其他部分相匹配，即使他们不知道自己在看什么。令人惊讶的是，这种预先强加的、有点盲目的数据组织往往对解释场景内容很有用。为了构建越来越灵活和适应性强的计算机视觉系统，我们必须设计它们，在它们开始理解所看到的东西之前，首先对视觉数据施加类似的组织。

A long-standing problem in computer vision is to be able to parse an image or a video into chunks which plausibly correspond to individual objects in the scene or, at least, parts of objects. For example, in 1983 two computer vision researchers, A. Witkin and J. Tenenbaum, explained how humans seem to impose an organization on visual scenes (images or video), thereby betting on which image parts go with other parts, even when they have no idea what they are looking at. The surprising thing is that this pre-imposed, somewhat blind, organization of the data often turns out to be useful for interpreting the contents of the scene. In order to build increasingly flexible and adaptable computer vision systems we must design them to first impose a similar organization on the visual data, before they begin to understand what they are looking at.