Grouping through local, parallel interactions

This paper describes a new approach for computer based visual grouping. A number of computational principles are defined related to results on neurophysiological and psychophysical experiments. The grouping principles have been subdivided into two groups. The 'first-order processes' perform local operations on 'basic' features such as luminance, color, and orientation. 'Second-order processes' consider bilocal interactions (stereo, optical flow, texture, symmetry). The computational scheme developed in this paper relies on the solution of a set of nonlinear differential equations. They are referred to as 'coupled diffusion maps'. Such systems obey the prescribed computational principles. Several maps, corresponding to different features, evolve in parallel, while all computations within and between the maps are localized in a small neighborhood. Moreover, interactions between maps are bidirectional and retinotopically organized, features also underlying processing by the human visual system. Within this framework, new techniques are proposed and developed for e.g. the segmentation of oriented textures, stereo analysis, optical flow detection, etc. Experiments show that the underlying algorithms prove to be successful for first-order as well as second-order grouping processes and show the promising possiblities such a framework can offer for a large number of low-level vision tasks.