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Image segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved significant results in medical, military, and industrial fields. Present research work mainly introduces the training of computer graphics and image processing technology and the method of active contour image segmentation. It focuses on the study of image segmentation methods and focuses on the segmentation methods based on active contour models. Firstly, it summarizes two types of segmentation methods based on edge and region and summarizes their advantages and disadvantages. Then, the segmentation method based on the active contour model is studied, and several typical active contour models are comprehensively compared. Finally, the local binary fitting model and the local Gaussian distribution fitting energy model are improved and simulated. Furthermore, from the development of computer graphics and image processing technology to analyze some methods and means of training this professional talent. The experimental results of this article show that the active contour image segmentation algorithm can not only ensure the image segmentation algorithm but also reduce the number of iterations and shorten the image segmentation time. Compared with the CV, LBF, and LGIF models computational efficiency of Segmentation method is increased by 9.2 times, 2.64 times, and 1.44 times, respectively.

Traditional methods of stripe noise removal based on space domain or transformation domain generally cannot handle the case where the noise is extremely sparse. To solve this problem, we propose a novel approach to accurately detect and remove the stripe noise by analyzing the directional and structural information of the stripe noise. First, we build a preselected stripe noise lines set by using local progressive probabilistic Hough transform. Subsequently, the real stripe noise lines are screened out from this set according to the feature of grayscale discontinuities. Finally, our approach uses the strategy of neighborhood grayscale weighted replacement and a local Gaussian filter to perform image destriping. Extensive experiments demonstrate that our approach proposed in this paper outperforms other recent promising methods in terms of quantitative assessments, qualitative assessments, and computing time.