Perceptual Grouping and Shape Abstraction

对象分类仍然是计算机视觉面临的最重要的挑战之一。当在图像中搜索特定的目标对象（目标检测问题）时，目标提供一个强大的形状，以帮助分割和解释图像特征。但是对于从大型数据库中进行对象分类的更一般的任务，没有这样的目标对象可用。相反，我们必须依赖于一套独立于对象的中级形状先验，这些先验反映了我们世界的规律——100年前格式塔心理学家发现的规律。这个问题可以表述如下。给定一个混乱场景的图像，并且没有对场景内容的先验知识，我们的第一个任务是将属于同一对象的图像特征分组在一起。这是一个经典的感知分组问题，近年来由于物体检测问题的突出，在很大程度上被视觉界所忽视，其中较强的物体层次先验包含较弱的中间层次先验。因此，本提案的主要目标之一是对这些中级形状先验进行建模，例如对称、连续性、连接和闭合，并开发可以检测这些规律的算法，并使用它们将场景分割为因果相关的图像特征集合，每个图像特征对应于不同的对象。

Object categorization continues to be one of the most important challenges facing computer vision. When a particular target object is searched for in an image (the object detection problem), the target provides a strong shape prior to help segment and interpret the image features. But for the more general task of object categorization from a large database, no such target object is available. Instead, we must rely on a set of object-independent, mid-level shape priors that reflect the regularities of our world -- regularities identified by the Gestalt psychologists 100 years ago. The problem can be formulated as follows. Given an image of a cluttered scene and no a priori knowledge of scene content, our first task is to group together image features that belong to the same object. This is the classical problem of perceptual grouping, which has been largely ignored by the vision community in recent years due to the prominence of the object detection problem, in which stronger object-level priors subsume weaker mid-level priors. One of the primary objectives of this proposal is therefore to model these mid-level shape priors, such as symmetry, continuity, junctions, and closure, and to develop algorithms that can detect these regularities and use them to segment a scene into causally-related collections of image features, each corresponding to a different object. Ultimately, we would like to recognize the objects that these feature collections depict. But herein lies the problem when dealing with categories that exhibit high within-class variation. The granularity of the features comprising a categorical model is very coarse, whereas the granularity of the image features comprising a collection is very fine. This disparity is often referred to as the semantic gap in computer vision. We must therefore abstract (or regularize) the groups of local image features in order to "lift" them up to the level of the coarse, prototypical features that make up the models in the database. This raises many critical research questions also addressed in this proposal: 1) how do we model the abstract shape of an object?; 2) what are the invariant parts whose detection can help us determine what object we may be looking at? (a problem known as object indexing); 3) how can we use knowledge of a vocabulary of abstract parts to drive the abstraction process that will help us recover such parts from an image?; and 4) should these parts be 2-D or 3-D? Our research program addresses the two important and closely related problems of perceptual grouping and shape abstraction on multiple fronts. Without any knowledge of scene content, the ability to recover from an image a set of abstract, part-based image features yields a powerful indexing mechanism that can prune a large database down to a small number of promising candidates which, in turn, can provide strong, top-down priors to segment and detect the objects in a scene.