
Research Article
Intelligent Extraction of Color Features in Architectural Space Based on Machine Vision
@INPROCEEDINGS{10.1007/978-3-031-50574-4_4, author={Zhengfeng Huang and Liushi Qin}, title={Intelligent Extraction of Color Features in Architectural Space Based on Machine Vision}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2024}, month={2}, keywords={Machine Vision Architectural Space Space Color Color Characteristics Feature Extraction Intelligent Extraction Extraction Method}, doi={10.1007/978-3-031-50574-4_4} }
- Zhengfeng Huang
Liushi Qin
Year: 2024
Intelligent Extraction of Color Features in Architectural Space Based on Machine Vision
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-031-50574-4_4
Abstract
Architecture itself has artistic characteristics, and architectural color will change with the change of environmental parameters. The analysis of the color characteristics of the building space is conducive to the color analysis and auxiliary design of the building. The current color feature extraction methods are prone to a sharp increase in signal-to-noise ratio, resulting in a low extraction rate. In order to solve the above problems, a new intelligent extraction method of building space color features is designed based on machine vision. The color information transmission channel model is established, and the transmission path function is introduced to obtain the mathematical model of intelligent extraction of building space color features. The intelligent extraction is realized through the reliability calculation model. Using machine vision, color information and texture information are organically combined to extract construction land information. The experimental results show that in the process of extracting color features, the extraction results of this method will not appear mutation points, the stability is well guaranteed, and the extraction accuracy can reach more than 99%, indicating that this method has good application effect.