1 March 1990 Hierarchical Local Symmetry: 2-D Shape Representation
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Proceedings Volume 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques; (1990) https://doi.org/10.1117/12.969735
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States
Abstract
The overall goal of our research is to build a vision learning system which can learn to classify objects from 2-D contour information. The visual representation method for such a vision learning system, called the hierarchical local symmetry (IILS), will be discussed in this paper. The definition and algorithms of smoothed local symmetry (SLS) is reviewed, which was introduced by Brady as a method satisfying stability versus sensitivity criteria of visual representation method. In this paper, HLS, modified SLS, are formalized and a new algorithm to compute the HLS is described. HLS eliminates some redundant information in the SLS and gives us hierarchical information. It also makes it possible to devise more efficient algorithms than that of SLS. Normalized polar coordinate representation (NITII) is used to store the computed HLS with translation, scale, and rotation invariance. Transforming the HLS into the NPCR where the learning process can be performed is also discussed.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kyugon Cho, Kyugon Cho, Stanley M. Dunn, Stanley M. Dunn, } "Hierarchical Local Symmetry: 2-D Shape Representation", Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969735; https://doi.org/10.1117/12.969735
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