一种图像分割模型的自适应参数选择方法
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摘要
针对灰度分布不均匀的图像特征,提出一种基于局部和全局高斯分布拟合能量的自适应权重参数选择方法。基于图像的局部和全局区域信息,以高斯分布作为拟合函数建立能量泛函。基于水平集方法,随着活动轮廓的演化,局部和全局区域信息在能量泛函中的权重会相应地变化,有利于提高图像分割的质量和效率。数值实验验证了该方法的有效性。
For the images characteristic with intensity inhomogeneity, this paper proposed an adaptive weight parameter selection method for local and global Gaussian distribution fitting energy. Based on the local and global region information of images, we established the energy function by employing the Gaussian distribution as the fitting function. According to the level set method, with the evolution of the active contour, the weight of the local and global region information in the energy function changed accordingly, which was conducive to improving the quality and efficiency of the image segmentation. Numerical experiments demonstrated the effectiveness of this method.
For the images characteristic with intensity inhomogeneity, this paper proposed an adaptive weight parameter selection method for local and global Gaussian distribution fitting energy. Based on the local and global region information of images, we established the energy function by employing the Gaussian distribution as the fitting function. According to the level set method, with the evolution of the active contour, the weight of the local and global region information in the energy function changed accordingly, which was conducive to improving the quality and efficiency of the image segmentation. Numerical experiments demonstrated the effectiveness of this method.
引文
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[2] Horritt M S.A statistical active contour model for SAR image segmentation[J].Image and Vision Computing,1999,17(3/4):213-224.
[3] Kass M,Witkin A,Terzopoulo D.Snakes:active contour models[J].International Journal of Computer Vision,1988,1(4):321-331.
[4] Osher S,Sethian J A.Fronts propagating with curvature-dependent speed:algorithms based on Hamilton-Jacobi formulation[J].Journal of Computational Physics,1988,79(1):12-49.
[5] Osher S,Tsai R.Level set methods and their applications in image science[J].Communications in Mathematical Sciences,2003,1(4):623-656.
[6] Caselles V,Kimmel R,Sapiro G.Geodesic active contours[J].International Journal of Computer Vision,1997,22(1):61-79.
[7] Chan T,Vese L.Active contours without edges[J].IEEE Transactions on Image Processing,2001,10(2):266-277.
[8] Lankton S,Tannenbaum A.Localizing region-based active contours[J].IEEE Transactions on Image Processing,2008,17(11):2029-2039.
[9] Li C,Kao C Y,Gore J C,et al.Minimization of region-scalable fitting energy for image segmentation[J].IEEE transactions on Image Processing,2008,17(10):1940-1949.
[10] Zhang K,Zhang L,Lam K M,et al.A level set approach to image segmentation with intensity inhomogeneity[J].IEEE Transactions on Cybernetics,2016,46(2):546-557.
[11] Wang H,Huang T-Z,Xu Z,et al.An active contour model and its algorithms with local and global Gaussian distribution fitting energies[J].Information Sciences,2014,263:43-59.
[12] Mumford D,Shah J.Optimal approximations by piecewise smooth functions and associated variational problems[J].Communications on Pure and Applied Mathematics,1989,42(5):577-685.