| Image segmentation is the process of dividing an image into several regions and extracting the object of interest.It is widely used in medical image processing,object recognition,video analysis,traffic management system monitoring and automatic operation.Variational image segmentation is achieved by constructing a certain energy functional and solving the corresponding minimization problem.How to numerically solve these variational models quickly and accurately has always been a hot and difficult problem in image segmentation.The convolution kernel approximation method is to approximate the contour length term in the variational segmentation model and design the corresponding numerical algorithm to achieve effective segmentation.It has been successfully applied in variational image segmentation methods.This paper studies the image segmentation method based on convolutional kernel approximation,and the main research contents and innovations are as follows:(1)The existing CVB model ignores the edge information of the target object when constructing the energy functional,and the result of image segmentation with intensity inhomogeneity is easy to appear the problem of over-segmentation or under-segmentation.Based on the CVB model,this paper introduces the length term based on geodesic contour to capture the edge information of the target object,and proposes the g-CVB model.(2)A fast numerical algorithm for solving the g-CVB model is studied.In this paper,a convolutional kernel approximation form for geodesic contour length terms is constructed,and an iterative algorithm based on alternating minimization and convolution threshold is designed to numerically solve the g-CVB model.Furthermore,the convergence and stability of the algorithm are analyzed.Numerical experiments show that the algorithm improves image segmentation accuracy and convergence speed.(3)A fast numerical algorithm for selective segmentation model RCI is studied.The existing algorithm for numerically solving RCI model is based on the additive operator splitting method,which has low computational efficiency.In this paper,convolution kernel is used to approximate geodesic length term in RCI model,and a fast algorithm for solving the model is designed.The experimental results show that the proposed algorithm can not only achieve the expected segmentation results,but also accelerates the convergence speed significantly. |
