Implementation of Gestalt Principles for Object Segmentation

Gestalt principles have been studied for about a century and were used for various computer vision approaches during the last decades, but became unpopular because the many heuristics employed proved inadequate for many real world scenarios. We show a new methodology to learn relations inferred from Gestalt principles and an application to segment unknown objects, even if objects are stacked or jumbled and tackle also the problem of segmenting partially occluded objects. The relevance of the relations for object segmentation is learned with support vector machines (SVMs) during a training period. We present an evaluation of the relations and show results of the segmentation framework.