8 March 2018 Dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106090E (2018) https://doi.org/10.1117/12.2283446
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
In order to extract target from complex background more quickly and accurately, and to further improve the detection effect of defects, a method of dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization was proposed. Firstly, the method of single-threshold selection based on Arimoto entropy was extended to dual-threshold selection in order to separate the target from the background more accurately. Then intermediate variables in formulae of Arimoto entropy dual-threshold selection was calculated by recursion to eliminate redundant computation effectively and to reduce the amount of calculation. Finally, the local search phase of artificial bee colony algorithm was improved by chaotic sequence based on tent mapping. The fast search for two optimal thresholds was achieved using the improved bee colony optimization algorithm, thus the search could be accelerated obviously. A large number of experimental results show that, compared with the existing segmentation methods such as multi-threshold segmentation method using maximum Shannon entropy, two-dimensional Shannon entropy segmentation method, two-dimensional Tsallis gray entropy segmentation method and multi-threshold segmentation method using reciprocal gray entropy, the proposed method can segment target more quickly and accurately with superior segmentation effect. It proves to be an instant and effective method for image segmentation.
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Li Li, Li Li, } "Dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090E (8 March 2018); doi: 10.1117/12.2283446; https://doi.org/10.1117/12.2283446
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