Infrared image segmentation based on gray-scale adaptive fuzzy clustering algorithm - Liu, Jin; Liu, Yanli; Ge, Qianqian (Multimedia tools and applications, 2017)
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.