Research On Color Image Segmentation Based On Partial Differential Equation And Level Set Method

Image segmentation is an important task for image processing,which is the foundation of high-level image processing,such as image analysis,image recognition and image understanding.The aim of segmentation is to divide an image into several non-overlapping sub-regions,and pixels in each region share a certain commonality.Segmentation methods based on partial differential equation and level set method,using the thought of dynamic evolution,have the superiority in image segmentation.The basic idea can be summarized as following.Firstly,an energy function about level set curve is defined,and then a partial differential equation controlling the curve evolution can be obtained by variational method,and the final evolution result is the image boundary.Adopting PDE-based image segmentation techniques and level set method;this paper makes some researches on color image as following:Firstly,the GAC-based gray image selective segmentation model of Gout et al.is expended to color image.In this paper,a given color image is treated as a whole and then the edge stopping function is defined.This model describes the geometric constraints by a distance function to selectively segment color images.During numerical implementation,the additive operator splitting algorithm is adopted to improve the segmentation speed.Segmentation results show the proposed model can selectively segment the interested region of a given color image.Secondly,considering that GAC-based color image selective segmentation has little accuracy and poor noise immunity.Considering these shortcomings,this paper improves the GAC-based color image selective segmentation model by adding region information to it.And the improved model can simultaneously make use of the edge information and region information.Secondly,this model adopts the inside area and outside area of the polygon formed by the condition points to describe the geometric constraints,instead of the distance function defined in the first model.This can help reduce computation amount.Segmentation results show that compared with the former model,this model has a better segmentation performance in three aspects.Firstly,this model enhances the noise robustness.Secondly,this model improves the segmentation speed compared with the former model,which means this model takes less time to segment the same image compared with the former model.Thirdly,this model can selectively segment some complex images which the former model fails to segment.Lastly,based on the improved model,this paper gets a dual level set color image segmentation model by defining two level set curves.One curve evolves to selectively segment image and the other curve evolves to globally segment the image.The geometric constraints are described by the inside area and the outside area of the polygon formed by the constraint points as the second model to pursue fast selective segmentation speed.Segmentation results show that this model can selectively and globally segment a color image at the same time.