標題: A generalized possibilistic approach to shell clustering of template-based shapes
作者: Wang, Tsaipei
Hung, Wen-Liang
資訊工程學系
Department of Computer Science
關鍵字: Diversity index;generic-template-based shape detection;possibilistic shell clustering;power-law relation
公開日期: Feb-2017
摘要: Efficiently and effectively detecting shell-like structures of particular shapes is an important task in computer vision and image processing. This paper presents a generalized possibilistic c-means algorithm (PCM) for shell clustering based on the diversity index of degree-lambda proposed by Patil and Taillie [Diversity as a concept and its measurement. J Amer Statist Assoc. 1982;77:548-561]. Experiments on various data sets in Wang [Possibilistic shell clustering of template-based shapes. IEEE Trans Fuzzy Syst. 2009;17:777-793] show that the the proposed generalized PCM performs better than Wang\'s [Possibilistic shell clustering of template-based shapes. IEEE Trans Fuzzy Syst. 2009;17:777-793] possibilistic shell clustering method according two two criteria: (i) the \'grade of detection\' g(d) for each target cluster; (ii) the amount of computation, denoted as k(c), required to attain a given g(d).
URI: http://dx.doi.org/10.1080/00949655.2016.1209202
http://hdl.handle.net/11536/132950
ISSN: 0094-9655
DOI: 10.1080/00949655.2016.1209202
期刊: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume: 87
Issue: 3
起始頁: 423
結束頁: 436
Appears in Collections:Articles