Amnon Shashua, Shimon Ullman
We describe in this paper a network that performs grouping of image con(cid:173) 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(cid:173) 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(cid:173) tour fragments into optimal groups. For example, certain subsets of contours spon(cid:173) 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 segmenting the image and selecting image structures that are likely to correspond to objects of interest in the scene.
'Ve propose that some form of autonomous grouping is performed at an early stage based on geometrical characteristics, that are independent of the identity of objects to be selected. The grouping process is governed by the notion of saliency in a way that priority is given to forming salient groups at the expense of potentially less salient ones. This general notion can again be illustrated by Fig. 1; it appears that certain groups spontaneously emerge, while grouping decisions concerning the less salient parts of the image may remain unresolved. As we shall see, the computation below exhibits a similar behavior.
We define a grouping of the image contours as the formation of a set of disjoint