Infrared image segmentation based on gray-scale adaptive fuzzy clustering algorithm

Since the infrared detector itself is subject to various external disturbances when collecting information, infrared images are characterized by of low SNR, low contrast and blur edge, which greatly increases the difficulty of detection and recognition. Contraposing the problems that a fuzzy cluster...

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Vydáno v:	Multimedia tools and applications Ročník 76; číslo 8; s. 11111 - 11125
Hlavní autoři:	Liu, Jin, Liu, Yanli, Ge, Qianqian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.04.2017 Springer Nature B.V
Témata:	Adaptive algorithms Algorithms Clustering Computer Communication Networks Computer Science Data Structures and Information Theory Decision support systems Image contrast Image segmentation Infrared detectors Infrared imagery Multimedia Information Systems Special Purpose and Application-Based Systems Adaptive fuzzy clustering Infrared image segmentation Multi-threshold segmentation Gray level histogram Pseudo-peak removal
ISSN:	1380-7501, 1573-7721
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Abstract	Since the infrared detector itself is subject to various external disturbances when collecting information, infrared images are characterized by of low SNR, low contrast and blur edge, which greatly increases the difficulty of detection and recognition. Contraposing the problems that a fuzzy clustering algorithm cannot find the reasonable clustering number adaptively, it will have a low infrared image segmentation rate when the gray-scale between object region and background region are of great difference. A gray-scale adaptive fuzzy clustering algorithm (GAFC) is proposed in this work. The methodology uses a coarse-fine concept to reduce the computational burden required for the fuzzy clustering and to improve the accuracy of segmentation that a single fuzzy clustering cannot reach. The coarse segmentation attempts to segment coarsely based on gray level histogram. Firstly, the pseudo peaks in the gray level histogram are removed by introducing a control factor of peak areas and a control factor of peak widths, then in order to find a finer segmentation result, the coarse segmentation result is clustered by an improved fuzzy clustering algorithm that introduces an adaptive function to get the most reasonable cluster number and that defines a logarithmic function as a measurement of distance. The results of experimental data show that not only the GAFC mentioned in this paper preserves the advantages in multi-threshold segmentation method which is fast and easy, and behaves well in segmenting infrared images in complex environments.
AbstractList	Since the infrared detector itself is subject to various external disturbances when collecting information, infrared images are characterized by of low SNR, low contrast and blur edge, which greatly increases the difficulty of detection and recognition. Contraposing the problems that a fuzzy clustering algorithm cannot find the reasonable clustering number adaptively, it will have a low infrared image segmentation rate when the gray-scale between object region and background region are of great difference. A gray-scale adaptive fuzzy clustering algorithm (GAFC) is proposed in this work. The methodology uses a coarse-fine concept to reduce the computational burden required for the fuzzy clustering and to improve the accuracy of segmentation that a single fuzzy clustering cannot reach. The coarse segmentation attempts to segment coarsely based on gray level histogram. Firstly, the pseudo peaks in the gray level histogram are removed by introducing a control factor of peak areas and a control factor of peak widths, then in order to find a finer segmentation result, the coarse segmentation result is clustered by an improved fuzzy clustering algorithm that introduces an adaptive function to get the most reasonable cluster number and that defines a logarithmic function as a measurement of distance. The results of experimental data show that not only the GAFC mentioned in this paper preserves the advantages in multi-threshold segmentation method which is fast and easy, and behaves well in segmenting infrared images in complex environments.
Author	Liu, Jin Liu, Yanli Ge, Qianqian
Author_xml	– sequence: 1 givenname: Jin surname: Liu fullname: Liu, Jin email: Jinliu@xidian.edu.cn organization: School of Electronic Engineering, Xidian University – sequence: 2 givenname: Yanli surname: Liu fullname: Liu, Yanli organization: School of Electronic Engineering, Xidian University – sequence: 3 givenname: Qianqian surname: Ge fullname: Ge, Qianqian organization: School of Electronic Engineering, Xidian University
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CitedBy_id	crossref_primary_10_1016_j_infrared_2021_103932 crossref_primary_10_1088_1361_6501_ab02db crossref_primary_10_1007_s10470_022_01987_3 crossref_primary_10_1007_s11042_018_6005_6
Cites_doi	10.1016/S0031-3203(01)00197-2 10.1109/34.85677 10.1016/0031-3203(90)90103-R 10.1109/TPAMI.2007.1046 10.1109/TSMCB.2012.2218233 10.1002/0471708607 10.1109/TIP.2009.2032942 10.1109/ICASSP.1984.1172729 10.1109/TITS.2009.2026674 10.1109/ICIP.2008.4712435 10.1109/TIP.2009.2032349 10.11834/jig.20100907 10.11591/telkomnika.v12i8.5716
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Keywords	Adaptive fuzzy clustering Infrared image segmentation Multi-threshold segmentation Gray level histogram Pseudo-peak removal
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SubjectTerms	Adaptive algorithms Algorithms Clustering Computer Communication Networks Computer Science Data Structures and Information Theory Decision support systems Image contrast Image segmentation Infrared detectors Infrared imagery Multimedia Information Systems Special Purpose and Application-Based Systems
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Title	Infrared image segmentation based on gray-scale adaptive fuzzy clustering algorithm
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