Artificial Intelligence Essay

59235 WordsMay 13, 2013237 Pages
Shape Decomposition for Graph Representation Cai Luyuan, Zhao Meng, Liu Shang, Mao Xiaoyan, and Bai Xiao Abstract. The problem of shape analysis has played an important role in the area of image analysis, computer vision and pattern recognition. In this paper, we present a new method for shape decomposition. The proposed method is based on a refined morphological shape decomposition process. We provide two more analysis for morphological shape decomposition. The first step is scale invariant analysis. We use a scale hierarchy structure to find the invariant parts in all different scale level. The second step is noise deletion. We use graph energy analysis to delete the parts which have minor contribution to the average graph energy. Our methods can solve two problems for morphological decomposition – scale invariant and noise. The refined decomposed shape can then be used to construct a graph structure. We experiment our method on shape analysis. Keywords: Graph spectra, Image shape analysis, Shape recognition. 1 Introduction Shape analysis is a fundamental issue in computer vision and pattern recognition. The importance of shape information relies that it usually contains perceptual information, and thus can be used for high level vision and recognition process. There has already many methods for shape analysis. The first part methods can be described as statistical modeling [4] [12][9] [11]. Here a well established route to construct a pattern space for the data–shapes is to Cai Luyuan School of Computer Science, China University of Geosciences, Wuhan, China Zhao Meng, Liu Shang, and Bai Xiao School of Computer Science and Engineering, Beihang University, Beijing, China Mao Xiaoyan Beijing Institute of Control Engineering, Beijing, China Roger Lee (Ed.): SNPD 2010, SCI 295, pp. 1–10, 2010. springerlink.com c Springer-Verlag Berlin Heidelberg 2010 2 C. Luyuan et al.

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