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Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions

Ananthi,VP, Balasubramaniam,P and Lim,CP 2014, Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions, Pattern Recognition, vol. 47, no. 12, pp. 3870-3880, doi: 10.1016/j.patcog.2014.07.003.

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Title Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions
Author(s) Ananthi,VP
Balasubramaniam,P
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Journal name Pattern Recognition
Volume number 47
Issue number 12
Start page 3870
End page 3880
Total pages 11
Publisher Elsevier BV
Place of publication Amsterdam , Netherlands
Publication date 2014-12-01
ISSN 0031-3203
Keyword(s) Hesitation degree
Intuitionistic fuzzy set
Membership function
Thresholding
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
RESTRICTED EQUIVALENCE FUNCTIONS
THRESHOLD SELECTION METHOD
ENTROPY
HISTOGRAM
ALGORITHM
FUZZINESS
Summary Segmentation is the process of extraction of objects from an image. This paper proposes a new algorithm to construct intuitionistic fuzzy set (IFS) from multiple fuzzy sets as an application to image segmentation. Hesitation degree in IFS is formulated as the degree of ignorance (due to the lack of knowledge) to determine whether the chosen membership function is best for image segmentation. By minimizing entropy of IFS generated from various fuzzy sets, an image is thresholded. Experimental results are provided to show the effectiveness of the proposed method.
Language eng
DOI 10.1016/j.patcog.2014.07.003
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier BV
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069994

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Citation counts: TR Web of Science Citation Count  Cited 11 times in TR Web of Science
Scopus Citation Count Cited 13 times in Scopus
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