Grouping contours by iterated pairing network

We describe in this paper a network that performs grouping of image con-tours. The input to the net are fragments of image contours, and the output is the partitioning of the fragments into groups, together with a saliency measure for each group. The grouping is based on a measure of overall length and curvature. The network decomposes the overall opti-mization problem into independent optimal pairing problems performed at each node. The resulting computation maps into a uniform locally connected network of simple computing elements. 1 The Problenl: Contour Grouping A problem that often arises in visual information processing is the linking of con-tour fragments into optimal groups. For example, certain subsets of contours spon-taneously form perceptual groups, as illustrated in Fig. 1, and are often detected immediately without scanning the image in a systematic manner. Grouping process of this type are likely to play an important role in object recognition by segmentin