Supervised classification of early perceptual structure in dot patterns
A supervised algorithm for computing perceptual groupings in dot patterns is presented. The algorithm uses shape features of the polygons in the Voronoi tessellation of the input pattern. The training patterns identified by humans are used to obtain an initial nocontextual classification which is then refined by a probabilistic relaxation labeling.<<ETX>>