27 March 2018 Feature recognition and detection for ancient architecture based on machine vision
Author Affiliations +
Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zheng Zou, Zheng Zou, Niannian Wang, Niannian Wang, Peng Zhao, Peng Zhao, Xuefeng Zhao, Xuefeng Zhao, } "Feature recognition and detection for ancient architecture based on machine vision", Proc. SPIE 10602, Smart Structures and NDE for Industry 4.0, 1060209 (27 March 2018); doi: 10.1117/12.2296543; https://doi.org/10.1117/12.2296543

Back to Top