摘要
传统的一维最大模糊熵图像分割算法对于图像的局部信息干扰噪声处理能力存在不足。文中研究实数编码混沌量子遗传算法(RCQGA)与一维模糊熵算法相结合的新算法。该算法将图像的空间信息和像素信息引入到一维模糊熵图像分割算法中,并运用实数编码混沌量子遗传算法对一维最大模糊熵图像分割算法进行改进,从而提高了一维最大模糊熵分割精度。研究结果表明,该算法分割效果明显优于传统一维模糊熵图像分割算法,并具有较强的抗噪性能。
Traditional one-dimension maximum entropy image segmentation algorithm based on fuzzy local information is obviously deficient in image noise processing capability. This paper proposes a new algorithm in combination of real-coded chaotic quantum genetic algorithm（RCQGA） with one-dimension fuzzy entropy algorithm. This method,by introducing the image spatial and pixel information in to the one-dimensional fuzzy entropy segmentation algorithm,could effectively enhance the accuracy of maximum fuzzy entropy segmentation. Research indicates that the algorithm is fairly better than traditional one-dimensional fuzzy-entropy image segmentation algorithm in segmentation results,and is of a strong anti-noise capability.
出处
《信息安全与通信保密》
2011年第9期67-69,共3页
Information Security and Communications Privacy
基金
国家自然科学基金资助项目(批准号:60971130)
关键词
图像分割
一维模糊熵
实数编码混沌量子遗传算法
空间信息
image segmentation
one-dimension fuzzy entropy
real-coded chaotic quantum-inspired genetic algorithm
spatial information
