Coarse-to-fine object recognition using shock graphs

Shock graphs have emerged as a powerful generic 2-D shape representation. However, most approaches typically assume that the silhouette has been correctly segmented. In this paper, we present a framework for shock graph-based object recognition in less contrived scenes. The approach consists of two steps, beginning with the construction of a region adjacency graph pyramid. For a given region, we traverse this scale-space, using a model shock graph hypothesis to guide a region grouping process that strengthens the hypothesis. The result represents the best subset of regions, spanning different scales, that matches a given object model. In the second step, the correspondence between the region and model shock graphs is used to initialize an active skeleton that includes a shock graph-based energy term. This allows the skeleton to adapt to the image data while still adhering to a qualitative shape model. Together, the two components provide a coarse-to-fine, model-based segmentation/recognition framework.