A bottom-up algorithm for finding principal curves with applications to image skeletonization

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• 作者：Liu ; Xiabi ; Jia ; Yunde
• 关键词：Principal curves ; Principal oriented points ; Image skeletonization ; Kernel smoother ; Minimum spanning tree
• 刊名：Pattern Recognition
• 出版年：2005
• 出版时间：July, 2005
• 年：2005
• 卷：38
• 期：7
• 页码：1079-1085
• 全文大小：346 K

文摘

This paper proposes a new method for finding principal curves from data sets. Motivated by solving the problem of highly curved and self-intersecting curves, we present a bottom-up strategy to construct a graph called a principal graph for representing a principal curve. The method initializes a set of vertices based on principal oriented points introduced by Delicado, and then constructs the principal graph from these vertices through a two-layer iteration process. In inner iteration, the kernel smoother is used to smooth the positions of the vertices. In outer iteration, the principal graph is spanned by minimum spanning tree and is modified by detecting closed regions and intersectional regions, and then, new vertices are inserted into some edges in the principal graph. We tested the algorithm on simulated data sets and applied it to image skeletonization. Experimental results show the effectiveness of the proposed algorithm.