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目标对象时（对象检测问题），目标在帮助分割和解释图像特征之前提供强形状。 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，我们必须依赖于一组独立于对象的中级形状先验，这些先验反映了我们世界的特征--100年前完形心理学家发现的特征。这个问题可以表述如下。 给定一个杂乱场景的图像，并且没有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。这是感知分组的经典问题，由于对象检测问题的突出性，近年来视觉界在很大程度上忽略了这个问题，其中较强的对象级先验压倒较弱的中级先验。 因此，本提案的主要目标之一是对这些中级形状先验（如对称性、连续性、连接和闭合）进行建模，并开发可以检测这些先验的算法，并使用它们将场景分割成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。 ** 最终，我们希望识别这些特征集合所描述的对象。但是，当处理表现出高类内变化的类别时，问题就在这里。构成分类模型的特征的粒度非常粗，而构成集合的图像特征的粒度非常细。 这种差异通常被称为计算机视觉中的语义差距。因此，我们必须抽象（或规则化）局部图像特征组，以便将它们“提升”到组成数据库中模型的粗糙原型特征的水平。这提出了许多关键的研究问题，也在这个建议中解决：1）我们如何建模一个对象的抽象形状？2)什么是不变的部分，它的检测可以帮助我们确定我们可能在看什么物体？（一个被称为对象索引的问题）; 3）我们如何使用抽象部分词汇表的知识来驱动抽象过程，这将帮助我们从图像中恢复这些部分？（4）这些部件应该是二维的还是三维的？** 我们的研究计划解决了感知分组和形状抽象在多个方面的两个重要和密切相关的问题。在没有任何场景内容的知识的情况下，从图像中恢复一组抽象的、基于部分的图像特征的能力产生了一种强大的索引机制，可以将大型数据库修剪成少量有希望的候选对象，这反过来又可以提供强大的、自上而下的先验来分割和检测场景中的对象。

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.**