Applying Dynamic Link Matching to Object Recognition in Real World Images

We apply the dynamic link matching algorithm to object recognition in gray level images. The algorithm is able to map from one view of an object to different-e. g., translated, rotated, or mirror-reflected—views, being at the same time tolerant of small distortions. A sparse representation (10%) of the image data is used as a boundary condition for a self-organizing mechanism which performs the object match within a modest number of iterations (~102). The mechanism can be derived from local neural dynamics [1].