Article Title
Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation
Keywords
kernel metric, image segmentation, local intensity information, convex optimization
Abstract
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.
Publisher
Tsinghua University Press
Recommended Citation
Gang Li, Haifang Li, Ling Zhang. Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation. Tsinghua Science and Technology 2018, 23(03): 303-314.
