Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation

查看全文 作　　者：Gang [1]Li;Haifang [1]Li;Ling [1]Zhang

机构地区：[1]College of Computer Science and Technology, Taiyuan University of Technology

出　　处：《Tsinghua Science and Technology》索引2018年第23卷第3期,共12页

基　　金：supported by the National Natural Science Foundation of China(No.61472270) 摘　　要：It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model's robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods. 关键词：图象分割模型基信息噪音内核度量标准挑战性孤立点

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Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation

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