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Edge-Weighted Centroidal Voronoi Tessellations 被引量：2

Edge-Weighted Centroidal Voronoi Tessellations
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摘要 Most existing applications of centroidal Voronoi tessellations(CVTs) lack consideration of the length of the cluster boundaries.In this paper we propose a new model and algorithms to produce segmentations which would minimize the total energy—a sum of the classic CVT energy and the weighted length of cluster boundaries.To distinguish it with the classic CVTs,we call it an Edge-Weighted CVT(EWCVT).The concept of EWCVT is expected to build a mathematical base for all CVT related data classifications with requirement of smoothness of the cluster boundaries.The EWCVT method is easy in implementation,fast in computation,and natural for any number of clusters. Most existing applications of centroidal Voronoi tessellations （CVTs） lack consideration of the length of the cluster boundaries. In this paper we propose a new model and algorithms to produce segmentations which would minimize the total energy -- a sum of the classic CVT energy and the weighted length of cluster boundaries. To distinguish it with the classic CVTs, we call it an Edge-Weighted CVT （EWCVT）. The concept of EWCVT is expected to build a mathematical base for all CVT related data classifications with requirement of smoothness of the cluster boundaries. The EWCVT method is easy in implementation, fast in computation, and natural for any number of clusters.
作者 Jie Wang Xiaoqiang Wang
机构地区 Department of Scientific Computing
出处 《Numerical Mathematics(Theory,Methods and Applications)》 SCIE 2010年第2期223-244,共22页 高等学校计算数学学报（英文版）
基金 supported in part by the U.S.National Science Foundation under grant number DMS-0913491.
关键词 Centroidal Voronoi tessellations cluster boundaD edge detection clustering image processing. 加权Voronoi图 电容式电压互感器 无级变速器 CVT变速器 应用程序 数学基础 数据分类 计算速度
分类号 TP317 [自动化与计算机技术—计算机软件与理论] P208 [天文地球—地图制图学与地理信息工程]
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参考文献32

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同被引文献16

  • 1曲吉林,寇纪淞,李敏强,安世虎.基于Voronoi图的异常检测算法[J].计算机工程,2007,33(23):35-36. 被引量：5
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  • 3Morris BT,Trivedi MM.Learning,modeling,and classification of vehicle track patterns from livevideo[J I.IEEE Transactions on Intelligent Trans- portation Systems, 2008,9(3 ) :425-437.
  • 4Yalcin H,Hebert M, Collins R.A flow-based approach to vehicle detection and backgroundmosaicking in airborne video [J].IEEE Computer Society Conference on Computer Society and Pattern Recognition,2005(2) : 1202.
  • 5Veeraraghavan H,Masoud O,Papanikolopoulos N.Vision-based monitoring of interseetions[C].IEEEInternational Conference on Intelligent Transport- ation Systems,2002:7-12.
  • 6Zhang Z,Cai Y,Huang K.Real-time moving object classification with automatic scene division[ C ].IEEE International Conference on Image P- rocessing, 2007 (5) : 149-152.
  • 7ViolaP,Jones M.Compositing Rapid object detection using a boosted cas- cade of simple features [J].IEEE Computer Vision & Pattern Recognition, 2001(1):I-511-I-518.
  • 8LiuL,Xing J,AiH.~lulti-view vehicle detection and tracking in crossroads [C].Pattern Recognition (ACPR),2011 First Asian Conference on.IEEE, 2011:608-612.
  • 9Du Q, Faber V, Gunzburger M.CentroidalVoronoi Tessellations: Applica- tions and Algorithms[J].Siam Review, 1999,41(4):637-676.
  • 10Wang J,Ju L,Wang X.An edge-weighted centroidalVoronoi tessellation model for image segrnentation[J].IEEE Transactions on Image Processi- ng,2009,18(8) : 1844-1858.

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Numerical Mathematics(Theory,Methods and Applications)
2010年 第2期

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