Improving Convergence in Hierarchical Matching Networks for Object Recognition

We are interested in the use of analog neural networks for recog(cid:173) nizing visual objects. Objects are described by the set of parts they are composed of and their structural relationship. Struc(cid:173) tural models are stored in a database and the recognition prob(cid:173) lem reduces to matching data to models in a structurally consis(cid:173) tent way. The object recognition problem is in general very diffi(cid:173) cult in that it involves coupled problems of grouping, segmentation and matching. We limit the problem here to the simultaneous la(cid:173) belling of the parts of a single object and the determination of analog parameters.