Minimal region extraction using expanding active contours

Segmenting images into objects is the first step towards object learning and recognition. The authors take a three-stage approach to this problem: (1) junctions and corners are detected from the image; (2) the minimal regions are extracted by applying an expanding `active snake´ model to detect edge contours through junctions and corners, resulting in an image composed of closed regions; and to (3) merge regions that are depth-continuous, and separate regions at the depth discontinuities, using constraints imposed by the junction types. In this paper the second step is described