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Fast and Robust Active Contours Model for Image Segmentation

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Abstract

Image segmentation using local region-based active contour models can segment images with intensity inhomogeneity effectively, but their segmentation results are sensitive to the initialization and easy to get incorrect results when dealing with texture images. This paper presents a novel active contour model (ACM) for image segmentation. The proposed method adopts local kernel mapping to enhance the discriminative ability to delineate nonlinear decision boundaries between classes. In addition, we introduce a modified convex model and propose a fast evolving scheme accordingly to deal with the minimization of the model energy function. The proposed approach is validated by a comparative study over a large number of experiments on synthetic and real images. The experiments demonstrate that our method is more efficient and robust for segmenting different kinds of images compared with the state-of-the-art image segmentation methods.

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Acknowledgements

This work has been partially supported by National Science Foundation of China (6100318, 61371168), National High Technology Research and Development Program of China (No. 2013AA014604), National key research and development program of China (2016YFC0801304, 2017YFC0803705), Jiangsu Province Regular Institutions of Higher Learning Academic Degree Graduate Student Innovation Plan (KYZZ16_0192).

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Authors and Affiliations

  1. The School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yupeng Li, Guo Cao & Xuesong Li

  2. China United Network Communications Corporation Jiangsu Branch, Nanjing, 210094, China

    Qian Yu

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  1. Yupeng Li
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  2. Guo Cao
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  3. Qian Yu
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  4. Xuesong Li
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Correspondence to Guo Cao.

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Li, Y., Cao, G., Yu, Q. et al. Fast and Robust Active Contours Model for Image Segmentation. Neural Process Lett 49, 431–452 (2019). https://doi.org/10.1007/s11063-018-9827-3

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