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

摘要

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.