Design of Visual Teaching System for Image Visualization Based on Deep Learning

Abstract

The education of computer graphics in teaching is mainly to make students have a systematic understanding of the subject, master the necessary theoretical knowledge and learn the commonly used graphics generation method. Computer graphics is not only the theoretical basis for learning other computer technologies. Although some algorithms are demonstrated by animation, learners can only see the pre-made content, and can't learn according to individual requirements, so they lack interactivity. In order to improve the application effect of computer graphics visualization teaching system, this study studies the image feature detection method based on DCNN, and proves the function realization of image processing visualization algorithm through simulation experiments. The test shows that compared with the traditional algorithm, the improved DCNN model in this study shows higher feature detection accuracy and efficiency in image visualization simulation. Visualization of computer images can greatly facilitate users to understand and improve their work efficiency, and greatly promote the rapid development of computer-aided instruction (CAI).