Adaptive CV model using convex optimization
基于凸优化的自适应CV模型*

The CV model can not accurately segment an image,and the segmentation speed is slow,and the model is very easy to trapp in local optimum.In order to solve the problems,this paper proposed a new adaptive CV model using convex optimization.First,to improve the accuracy of fitting center,it introduced the adaptive weight and used the weighted average to caculate the fitting center.Second,convex optimization was added to the new model to get global minima.Finally,the Split Bregman method could effectively improve the segmentation speed.Experimental results demonstrate that the proposed algorithm can get the more accurate and efficient segmentation results,and it is robust to the choice of initialization.