Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology

Xinghuo Ye,Qianyi Wang

doi:10.1109/access.2020.3022011

Abstract

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.

Highlights

In recent decades, people have done a lot of in-depth research on image segmentation technology, and hundreds of image segmentation methods have been widely studied and applied in various fields of computer vision
The accuracy rate is 78%; in the retrieval of dinosaurs, a total of 45 retrieved images and 35 related images are obtained, and the search accuracy rate is 72.5%. It can be seen from the experimental results that the method of the paper design has improved accuracy compared with the original method [27]. It can be seen from the figure that 58 students out of 100 students are very satisfied with the talent training model of computer graphics and image technology processing, accounting for 58% of the total, and they are satisfied with the talent training model of graphics and image technology processing
This study focuses on the methods of the active contour model, including the parametric active contour model and geometric active contour model

Summary

INTRODUCTION

People have done a lot of in-depth research on image segmentation technology, and hundreds of image segmentation methods have been widely studied and applied in various fields of computer vision. The geometric active contour model (GACM) is proposed on the basis of the combination of the level set method and curve evolution theory [4]. The disadvantage of this method is that it is prone to edge leakage Since this method did not conduct a deeper study on the energy function optimization of ACM and other issues, it only combined the level set method and curve evolution . On this basis, the researchers quickly proposed many related improvement models [7]

IMAGE SEGMENTATION METHOD

ANALYSIS OF TRAINING TALENTS FOR GRAPHICS AND IMAGE PROCESSING TECHNOLOGY

Findings

CONCLUSION