An Adaptive Fuzzy Clustering Algorithm Based on Multi-threshold for Infrared Image Segmentation

作者：Liu Jin^*; Liu Yanli; Ge Qianqian

来源：1st Chinese Conference on Computer Vision (CCCV), 2015-09-18 to 2015-09-20.

DOI：10.1007/978-3-662-48558-3_28

• Adaptive fuzzy clustering
• Infrared image segmentation
• Multi-threshold segmentation
• Pseudo-peak removal

To obtain the satisfied performance of infrared image segmentation in complex environments, an adaptive fuzzy clustering algorithm based on multi-threshold (AFC_MT) is proposed. 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 using the multi-thresholding technique. Firstly, the pseudo peaks in a multi-threshold algorithm are removed by introducing a control factor of peak areas and a control factor of peak widths to segment an image coarsely, 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. Experimental results show that AFC_MT behaves well in segmenting infrared images in complex environments.

• 出版日期2015
• 单位西安电子科技大学