A method for identifying groups of intensity edges in an image that are likely to result from the same convex object in a scene is described. A key property of the method is that its output is no more complex than the original image. The method uses a triangulation of linear edge segments to define a local neighborhood that is scale invariant. From this local neighborhood a local convexity graph that encodes which neighboring image edges could be part of a convex group of image edges is constructed. A path in the graph corresponds to a convex polygonal chain in the image, such as a convex polygon or a spiral. Examples are presented to illustrate that the technique find intuitively salient groups.>

A method is presented for efficiently maintaining and searching a database of three-dimensional models so they can be reliably recognized from arbitrary two-dimensional projections in the presence of noise and occlusion. The core of the process is the topologically defined network of invariants which breaks three-dimensional models down into small, local groups of features and indexes these groups using functions that are invariant under translation, rotation, scaling, and orthographic projection. The network encodes the geometrical relationships between these groups so that grouping information can be used to increase the speed of matching.>