Abstract
Infrared image segmentation is widely used in engineering, and it is a very important research topic. In this paper, the two-dimensional minimum Tsallis cross entropy and artificial immune algorithm is organically combined for infrared image segmentation optimization, through clonal selection to enhance optimal threshold search ability, and introduce a high frequency variation and population update to prevent the algorithm into a local optimum degradation. The preliminary test results show that the method is simple and robust, and can obtain good segmentation results.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 51275518).
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Zhang, JY. (2018). Infrared Thermal Wave Image Segmentation Based on 2D Tsallis Entropy and Artificial Immune Algorithm. In: Qiao, F., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2017. Advances in Intelligent Systems and Computing, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-319-70990-1_71
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DOI: https://doi.org/10.1007/978-3-319-70990-1_71
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