✍ Scribed by J.-H. Chuang; P.-H. Wang; M.-C. Wu
No coin nor oath required. For personal study only.
This paper presents a classi®cation scheme for 3D block-shaped parts. A part is block-shaped if the contours of its orthographic projections are all rectangles. A block-shaped part is classi®ed based on its partitioned view-contours, which are the result of partitioning the contours of its orthographic projections by visible or invisible projected line segments. The regions and their adjacency in a partitioned view-contour are ®rst converted to a graph, then to a reference tree, and ®nally to a vector form, with which a back-propagation neural network classi®er can be trained and applied. The proposed back-propagation neural network classi®er is in a cascaded structure and has advantages that each network can be limited to a small size and trained independently. Based on the classi®cation results on their partitioned view-contours, parts are grouped into families that can be in one of the three levels of similarity. Extensive empirical tests have been performed; the pros and cons of the approach are also investigated.
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