A neural network approach to planar-object recognition in 3D space
Most existing 2D object recognition algorithms are not perspective (or projective) invariant, and hence are not suitable far many real-world applications. By contrast, one of the primary goals of this research is to develop a flat object matching system that can identify and localise an object, even when seen from different viewpoints in 3D space. In addition, we also strive to achieve good scale invariance and robustness against partial occlusion as in any practical 2D object recognition system. The proposed system uses multi-view model representations and objects are recognised by self-organised dynamic link matching. The merit of this approach is that: it offers a compact framework for concurrent assessments of multiple match hypotheses by promoting competitions and/or co-operations among several local mappings of model and test image feature correspondences. Our experiments show that the system is very successful in recognising object to perspective distortion, even in rather cluttered scenes.
neural networks object recognition dynamic link matching gabor wavelets perspective transformation simulated annealing