基于二维最大模糊熵和智能遗传算法的红外图像分割

2004 International Conference on Intelligent Mechatronics and Automation, 2004. Proceedings. Pub Date : 2004-08-26 DOI:10.1109/ICIMA.2004.1384223

Jin Wu, Juan Li, Jian Liu, J. Tian

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引用次数: 0

摘要

摘要本文提出了一种快速有效的分割方法。基于模糊滤波、二维最大模糊熵原理的前视红外(FLIR)成像方法。和。智能遗传算法。采用一种新的模糊算子对噪声图像进行增强。基于模糊关系和最大熵原理，对二维直方图进行模糊划分。提出了一种新的智能遗传算法(IGA)，该算法采用基于正交阵列(OAS)的智能交叉(IC)来搜索图像参数的最优组合，最终用于图像分割。实验结果表明，该方法能有效地抑制高斯类噪声，实现红外图像的自动准确分割，并且比传统的遗传算法和穷举搜索算法更快。

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Infrared image segmentation based on 2-D maximum fuzzy entropy and intelligent genetic algorithm

AbsimctThis paper presents a fast and effective segmentation. method for Forward Looking Infra-Red (FLIR) 1magei-based:on fuzzy filtering, the principle of 2-D maximum fuzzy entropy. and. intelligent genetic algorithm. A new fuzzy operator is applied Wthe enhancement of noisy images. Basing on fuzzy relation and maximum l u n y entropy principle, we perform fnzzy partition on a 2-D histogram. A new intelligent genetic algorithm (IGA), which applies an intelligent crossover (IC) based on orthogonal arrays (OAS), Is proposed to search the .optimal wmbination of the fumy parsmetem finally used to segment the image. Experiment results show that the proposed method can depress the Gaussian (-like) noise effectively, segment the infrared image automatically and properly, and i s faster than the conventional genetic algorithm and exhaustive search.

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2004 International Conference on Intelligent Mechatronics and Automation, 2004. Proceedings.

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