A computational examination of image segmentation and the initial stages of human vision.

A novel approach to the question of image segmentation is considered. Instead of relying on edge-detection mechanisms to encircle the image of an object, it is proposed that the general Gestalt-like properties of images of objects can be used. These manifest themselves as particularly simple properties at relatively coarse spatial scales when the image is filtered with orientation-selective filters. The first part of the paper is concerned with a computational analysis of this proposal. A key issue is the question of how the information in filtered images is extracted. A simple primitive parametric description of zero-bounded blobs is used in this paper. It is shown how such a scheme can support readily the clustering of features with Gestalt-like properties. In the second part of the paper, two general difficulties for vision that are particularly severe for the present scheme are considered. The first of these are the effects of strongly asymmetric illumination. The second are the effects of using a cluttered scene. In each case the nature of the problem is examined and the nature of the computational solution considered.