Grouping Based on Coupled Diffusion Maps

Systems of coupled, non-linear diffusion equations are proposed as a computational tool for grouping. Grouping tasks are divided into two classes - local and bilocal - and for each a prototypical set of equations is presented. It is shown how different cues can be used for grouping given these two blueprints plus cue-specific specialisations. Results are shown for intensity, texture orientation, stereo disparity, optical flow, mirror symmetry, and regular textures. The proposed equations are particularly well suited for parallel implementations. They also show some interesting analogies with basic architectural characteristics of the cortex.

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