A dynamic method to reduce the search space for visual correspondence problems
Christoph von der Malsburg, Junmei Zhu · 2004
This dissertation presents a fast self-organizing system for solving the visual correspondence problem, the creation of a mapping between corresponding points in two images with variations in position, scale; orientation, and deformation. The starting point for our system is Dynamic Link Matching (DLM). Dynamic links are implemented as synapses with rapid plasticity that can switch under the control of signal correlations on a time scale of 100 milliseconds. The links serve to connect corresponding points in the two images, and they self-organize to form continuous one-to-one mappings. DLM can deal with relative shift and is robust against deformation. It presupposes no learning or specific circuit structures, letting it appear as a plausible basis for early post-natal vision. But DLM requires thousands of iterations so it is not a viable model for fast adult object recognition. Moreover, DLM seems not to be able to deal with transformations too big to be considered as a mere deformation. Our new implementation requires direct interactions between links. These interactions are modeled with the help of maplets, which stand for local groups of links that are consistent with each other in terms of transformation parameters (position, orientation and scale). Links and maplets form a cooperative dynamic system that has the ability to converge very quickly on globally consistent mappings. In this process, feature similarities between the two images favor the correct correspondences. Experiments show that a mapping can be formed in just a few iterations, irrespective of differences in scale and in-plane rotation. This speed is due to the long range and specificity of link-to-link connections. We also show that maplets and their connections can be learned by generalized Hebbian plasticity from consistent mappings, presumably formed by classical DLM. In simple pilot experiments the maplets formed by learning closely resemble those previously designed manually. Associative learning of link-to-link connections promises to extend the current system to more general variations (involving, e.g., rotation in depth).