A generalized possibilistic approach to shell clustering of template-based shapes

Tsaipei Wang; Wen Liang Hung

doi:10.1080/00949655.2016.1209202

A generalized possibilistic approach to shell clustering of template-based shapes

Tsaipei Wang, Wen Liang Hung^*

^*Corresponding author for this work

Institute of Multimedia Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

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-λ 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.

Original language	English
Pages (from-to)	423-436
Number of pages	14
Journal	Journal of Statistical Computation and Simulation
Volume	87
Issue number	3
DOIs	https://doi.org/10.1080/00949655.2016.1209202
State	Published - 11 Feb 2017

Keywords

Diversity index
generic-template-based shape detection
possibilistic shell clustering
power-law relation

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10.1080/00949655.2016.1209202

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title = "A generalized possibilistic approach to shell clustering of template-based shapes",

abstract = "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-λ 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 {\textquoteleft}grade of detection{\textquoteright} gd for each target cluster; (ii) the amount of computation, denoted as kc, required to attain a given gd.",

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N2 - 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-λ 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’ gd for each target cluster; (ii) the amount of computation, denoted as kc, required to attain a given gd.

AB - 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-λ 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’ gd for each target cluster; (ii) the amount of computation, denoted as kc, required to attain a given gd.

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A generalized possibilistic approach to shell clustering of template-based shapes

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Keywords

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