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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Vydané v:Multimedia tools and applications Ročník 76; číslo 8; s. 11111 - 11125
Hlavní autori: Liu, Jin, Liu, Yanli, Ge, Qianqian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.04.2017
Springer Nature B.V
Predmet:
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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Shrnutí: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.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-016-3657-y