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Quasi-Bezier curves integrating localised information

集成的 Quasi-Bezier 曲线本地化信息

作     者:Sohel, FerdoUs A. Karmakara, Gour C. Dooley, Laurence S. Arkinstall, John R. 

作者机构:Monash Univ Gippsland Sch Informat Technol Churchill Vic 3842 Australia Monash Univ Sch Engn & Appl Sci Churchill Vic 3842 Australia 

出 版 物:《PATTERN RECOGNITION》 (图形识别)

年 卷 期:2008年第41卷第2期

页      面:531-542页

核心收录:

学科分类:0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

主  题:vertex-based shape coding image processing video processing bezier curve 

摘      要:Bezier curves (BC) have become fundamental tools in many challenging and varied applications, ranging from computer-aided geometric design to generic object shape descriptors. A major limitation of the classical Bezier curve, however, is that only global information about its control points (CP) is considered, so there can often be a large gap between the curve and its control polygon, leading to large distortion in shape representation. While strategies such as degree elevation, composite BC, refinement and subdivision reduce this gap, they also increase the number of CP and hence bit-rate, and computational complexity. This paper presents novel contributions to BC theory, with the introduction of quasi-Bezier curves (QBC), which seamlessly integrate localised CP information into the inherent global Bezier framework, with no increase in either the number of CP or order of computational complexity. QBC crucially retains the core properties of the classical BC, such as geometric continuity and affine invariance, and can be embedded into the vertex-based shape coding and shape descriptor framework to enhance ratedistortion performance. The performance of QBC has been empirically tested upon a number of natural and synthetically shaped objects, with both qualitative and quantitative results confirming its consistently superior approximation performance in comparison with both the classical BC and other established BC-based shape descriptor methods. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

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