Perceptual Grouping and Shape Abstraction

目标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较强的形状。但是对于来自大型数据库的更一般的对象分类任务，没有这样的目标对象可用。取而代之的是，我们必须依赖于一套独立于对象的、中层的形状先验，它们反映了我们世界的规则--100年前格式塔心理学家确定的规则。这个问题可以表述如下。在给定一幅杂乱场景的图像并且没有场景内容的先验知识的情况下，我们的首要任务是将属于同一对象的图像特征组合在一起。这是知觉分组的经典问题，近年来由于目标检测问题的突出，这一问题在很大程度上被视觉界忽视了，在该问题中，较强的对象级先验包含较弱的中级先验。因此，该建议的主要目标之一是对这些中层形状先验(例如对称性、连续性、交汇点和闭合)进行建模，并开发能够检测这些规律性并使用它们将场景分割成因果相关的图像特征集合的算法，每个图像特征集合对应于不同的对象。

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.