基于有偏场的光栅图像模糊聚类分割算法  

A Modified Fuzzy C-Means Segmentation Algorithm for Illumination Pattern-based Images under Bias Field

作　　者：马莉[1] 赵树升[2] 朱磊[2] 

机构地区：[1]杭州电子科技大学自动化学院,浙江杭州310018 [2]郑州大学升达经贸管理学院,河南郑州450052

出　　处：《计算机应用研究》2006年第4期135-136,139,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(60234030);河南省教育厅自然科学基金资助项目(20025100006)

摘　　要：针对有偏场环境下带有光栅的散焦图像分割问题,提出了一种新的基于有偏场估计的模糊聚类分割算法。通过建立依赖于有偏场的模糊聚类目标函数,导出了基于灰度以及邻域灰度均值的聚类中心、模糊聚类函数以及有偏场估计的迭代算法;并在该算法生成的初始分割基础上,利用膨胀算子对分类结果进行细化。该方法较好地处理了传统模糊聚类对有偏场下光栅图像分割精度下降的问题。实验结果表明,基于有偏场的模糊聚类算法能有效分割光栅图像,其分割精度优于传统模糊聚类和阈值法。A novel FCM segmeatation algorithm is proposed based on bias field estimation with respect to the segmentation issue of defocused images with illumination patterns under bias field, Firstly the recursive algorithms are provided for bias field estimation, clustering centers and fuzzy Clustering function based on intensity and average intensity of pixel neighborhood based on object function of image model with intensity in homogeneity, A dilation operator is then used on initial segmented image for further refinement. The problem of reduced segmentation accuracy with traditional FCM is well solved by the proposed approach. Experiment results show that the proposed method is effectiveness for illumination pattern based images under bias field and of higher accuracy rates than traditional FCM and thresholding.

关 键 词：模糊聚类 图像分割 有偏场 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]