Dynamic link architecture for matching planar objects in three-dimensional space

Most prior works in neural networks for object matching focus largely on 2D problems only and often assume highly-constrained environments. In contrast, this paper takes a more generalized approach for planar object matching which is invariant to 3D perspective transformation and partial occlusion. The object´s domain is not restricted to purely 2D items; it includes fairly flat real objects such as a pair of scissors. The proposed system uses multi-view model representations and objects are recognized by self-organized 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 or co-operations among several local mappings of model and test image feature correspondences. A wide spectrum of rigorous test has been applied to the proposed system. Experimental results have demonstrated the system´s ability to produce accurate matches even when the test image is cluttered with irrelevant features