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An Adaptive Stopping Active Contour Model for Image Segmentation
Yuefeng Niu,Jianzhong Cao,Zuofeng Zhou 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.1
Active contour models (ACMs) are widely used in image segmentation applications. However, the selection of maximum iterations which controls the convergence of the ACMs is still a challenging problem. In this paper, an adaptive method for choosing the optimal number of iterations based on the local and global intensity fitting energy is proposed, which increases the automaticity of the active contour model. Moreover, the adoption of the reaction diffusion (RD) method instead of the distance regularization term can improve the accuracy and speed of segmentation effectively. Experimental results on synthetic and real images show that the proposed model outperforms other representative models in terms of accuracy and efficiency.