Intelligent Computer Technology-Driven Mural Pattern Recognition Method. (17th November 2022)

• Record Type: Journal Article
• Authors: Wei, Wenqing; Gao, Lei
• Other Names: Zhou Tao Academic Editor.
• Is Part Of: Advances in multimedia. Volume 2022(2022)
• Publication Date: 2022-11-17
• Subjects: Multimedia systems -- Periodicals; Computer networks -- Periodicals; Multimédia; Réseaux d'ordinateurs; Computer networks; Multimedia systems; Periodicals; 006.7
• DOI: 10.1155/2022/6148192
• Languages: English
• ISSNs: 1687-5680

Abstract

Abstract : As an important part of cultural heritage, murals reflect the economy, culture, and ideas of different historical periods and are an important basis for historical research. The lines in murals are the core elements to express the beauty of images. They have an irreplaceable special position in murals and are of great significance in the protection and restoration of murals. With the development of image recognition technology, the recognition of mural images has become a key research topic. In recent years, as a new image processing technology, deep learning based on a convolutional neural network is widely used in many fields. Using a convolutional neural network to recognize images has become a very active topic. With the continuous deepening of the number of layers of the convolutional neural network model, its autonomous learning ability of image recognition continues to improve. However, there are still some problems in the current image recognition model based on a convolutional neural network for mural images with rich structural details and complex texture and color. Therefore, according to the texture and structural characteristics of mural images, this paper uses the design idea of a convolutional neural network for reference to carry out research on mural image recognition. The improved algorithm proposed in this paper is tested on the experimental data set of mural images. The experimental results show that the improved algorithm can reduce the recognition error; enhance the edge, texture, and structure information of the reconstructed mural image; and enrich the detail information of the reconstructed mural image.