An Adaptive C-V Image Segmentation Model Guided by Gray Difference Energy Function
WANG Xiang-Hai1,2, WANG Jin-Ling1 , FANG Ling-Ling1,2
1. School of Computer and Information Technology, Liaoning Normal University, Dalian 116029 2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006
Abstract：As the sign of geometric active contour model (GACM), the C-V model has robustness to obscured targets and edge noise in image segmentation. However, this model usually cannot deal with complex heterogeneous images, and it is also sensitive to the initial position of evolution curve and has a high computational complexity. The more the average gray difference between inner region and outer region is, the closer the evolutionary curve to accurate target edge is. On this basis, an adaptive C-V image segmentation model guided by gray difference energy function is proposed. The model can adjust the movement trend of the evolutionary curve by the guidance function constructed based on average gray difference between inner region and outer region adaptively. This makes the evolution of the curve within a valid narrow band scope. The proposed model ensures the local homogeneity of gray calculation of contour curve between inner region and outer region and enhances the ability to capture the detailed target. At the same time, it improves the calculation speed of the model and the adaptability to the initial position of evolution curve to a certain extent. A large number of simulation experiments verify the validity of the proposed model.
王相海，王金玲，方玲玲. 灰度差能量函数引导的图像分割自适应C-V模型*[J]. 模式识别与人工智能, 2015, 28(3): 214-222.
WANG Xiang-Hai, WANG Jin-Ling , FANG Ling-Ling. An Adaptive C-V Image Segmentation Model Guided by Gray Difference Energy Function. , 2015, 28(3): 214-222.
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2015, Vol. 28 
