Parallel Approach to Binocular Stereo Matching
Herbert Jahn · elib (German Aerospace Center) · 2002
An approach for parallel-sequential binocular stereo matching is presented. It is based on discrete dynamical models which can be implemented in neural multi-layer networks. It is based on the idea that some features (edges) in the left image exert forces on similar features in the right image in order to attract them. Each feature point (i,j) of the right image is described by a coordinate x(i,j). The coordinates obey a system of time discrete Newtonian equations of motion, which allow the recursive updating of the coordinates until they match the corresponding points in the left image. That model is very flexible. It allows shift, expansion and compression of image regions of the right image, and it takes into account occlusion to a certain amount. To obtain good results a robust and efficient edge detection filter is necessary. It relies on a non-linear averaging algorithm which also can be implemented using discrete dynamical models. Both networks use processing elements (neurons) of different kind, i.e. the processing function is not given a priori but derived from the models. This is justified by the fact that in the visual system of mammals (humans) a variety of different neurons adapted to specific tasks exist. A few examples show that the problem of edge preserving smoothing can be solved with a quality which is sufficient for many applications (various images not shown here have been processed with good success). A certain success was also achieved in the main problem of stereo matching but further improvements are necessary.