Probabilistic Models for Image and Video Parsing

计算机视觉中的一个长期存在的问题是，如何将一幅图像或一段视频解析成与场景中的单个对象或至少对象的一部分相对应的块。例如，两名计算机视觉研究人员A.Witkin和J.Tenenbaum在1983年解释了人类似乎如何在视觉场景(图像或视频)上强加一种组织，从而押注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，这种预先设定的、有些盲目的数据组织往往被证明对解释场景的内容很有用。为了建立越来越灵活和适应性更强的计算机视觉系统，我们必须首先对它们进行设计，以便在它们开始理解它们正在看的东西之前，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.