Research Of Image Segmentation Method Based On CV Model

Image segmentation is one of the basic concepts for accomplishing tasks such as image processing and analysis.At the same time,segmentation is also attributed to a kind of computer vision technology.Since we have raised the issue of division,we have been advancing the rapid development of segmentation technology.The focus of this study is the newly developed CV model.The CV model is an algorithm developed on the basis of partial differential equations.It is an image segmentation model that belongs to the level set method based on regional features.The Chan-Vese(CV)model is a promising active contour model for image segmentation.The segmentation of CV model is achieved by applying the level set.It promotes the evolution of the curve by the level set.The CV model finally stops the evolution process at the boundary position of the target,and the CV model gets the segmentation result.After deeply understanding and exploring this algorithm,we found that the traditional CV model has the problem of being sensitive to the initial contour line,dividing the object away from the contour line,and the problem of large calculation volume.In order to overcome the limitations of the CV model,this paper proposes for several improvements,we summarize as follows:(1)In this paper,a suitable initial contour is determined so that the CV model segmentation will not be constrained by the initial contour line,and the CV model segmentation effect is good.(2)In this paper,energy penalties are integrated into the CV model.This method avoids the initialization of the model once again,so we greatly reduce the evolution time of the level set.(3)This paper proposes a new geometric active contour model based on the energy minimization strategy.In this paper,we use the distance penalty energy function to regularize the level set function in the new energy.Finally,we use the experiments to verify the improved algorithm of this paper.This paper compares it with other segmentation algorithms.The conclusion we have drawn is: The improved method of this article has greatly improved the CV without amplifying software and hardware.Adaptability of model segmentation range.The main contributions of this article are summarized in two aspects.One is based on a minimized new energy function and the other is the introduction of a suitable initial profile.In summary,the method proposed in this paper alleviates the problem of unsatisfactory segmentation duration and segmentation results.This method provides a good theoreticalfoundation and reference for related parameters.This method is of great significance and application value for solving practical segmentation problems in the future.