Abstract
Image segmentation is to segment images into subdomains with same intensity, texture or color. Texture is one of the most important features to images. Because of the complexity of texture, segmentation of texture image is especially difficult and it seriously restricts the development of image processing. In this paper, a nonlocal Mumford-Shah (NLMS) model is proposed to segment multiphase texture images. This proposed model uses nonlocal operators that are capable of handling texture information in the image. In order to segment different patterns of texture simultaneously, multiple region partition strategy which uses n label functions to segment n+1 texture regions is adopted. Furthermore, to improve computational efficiency, the proposed model avoids directly computing the resulting nonlinear partial differential equation (PDE) by using Split Bregman algorithm. Numerical experiments are conducted to validate the performance of proposed model.
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Lu, W., Duan, J., Wei, W., Pan, Z., Wang, G. (2015). Nonlocal Mumford-Shah Model for Multiphase Texture Image Segmentation. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Di, K. (eds) Advances in Image and Graphics Technologies. IGTA 2015. Communications in Computer and Information Science, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47791-5_43
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DOI: https://doi.org/10.1007/978-3-662-47791-5_43
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