This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of t...This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese(CV) model, while the image’s local entropy is used to establish the index for measuring the image’s gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation process.Compared with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local terms.Experiments show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.展开更多
基金supported by the National Natural Science Foundation of China(No.61876124)
文摘This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese(CV) model, while the image’s local entropy is used to establish the index for measuring the image’s gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation process.Compared with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local terms.Experiments show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.
