基于遗传模糊C-均值与概率松弛法的图像分割研究
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国家自然科学基金项目(No.50403005);华东理工大学青年骨干教师项目(No.0156101)资助


Image Segmentation Based on GA-FCM Clustering and Probability Relaxation
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    摘要:

    在利用遗传模糊C-均值对图像像素进行初步分类的基础上,采用概率松弛算法对目标与背景间的疑似像素进行进一步分割和目标提取,很好地解决了目标提取不完整的问题,实验结果表明,该算法具有良好的特性。

    Abstract:

    Image segmentation is an essential approach for image processing.For some complex images,the extracted objects usually have a problem of incomplete edge or broken boundary.To solve this problem,we first apply fuzzy C-means clustering method based on genetic algorithm (GA-FCM) to segment the image pixels into different regiments.Besides background pixels and definite object pixels,union operation shows that the pixels around broken area are uncertain to be classified into object or background.We present probability relaxation (PR) algorithm to further segment the uncertain pixels according to their statistic properties.Experimental results indicate that this algorithm well solved the above problem and is effective for image segmentation and object extraction.

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朱煜,江林佳.基于遗传模糊C-均值与概率松弛法的图像分割研究[J].激光与红外,2008,38(4):392~395
ZHU Yu, JIANG Lin-jia. Image Segmentation Based on GA-FCM Clustering and Probability Relaxation[J]. LASER & INFRARED,2008,38(4):392~395

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