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Feature recognition and detection for ancient architecture based on machine vision
Author(s): Zheng Zou; Niannian Wang; Peng Zhao; Xuefeng Zhao
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Paper Abstract

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.

Paper Details

Date Published: 27 March 2018
PDF: 7 pages
Proc. SPIE 10602, Smart Structures and NDE for Industry 4.0, 1060209 (27 March 2018); doi: 10.1117/12.2296543
Show Author Affiliations
Zheng Zou, Dalian Univ. of Technology (China)
Niannian Wang, Dalian Univ. of Technology (China)
Peng Zhao, Beijing Palace Museum (China)
Xuefeng Zhao, Dalian Univ. of Technology (China)

Published in SPIE Proceedings Vol. 10602:
Smart Structures and NDE for Industry 4.0
Norbert G. Meyendorf; Dan J. Clingman, Editor(s)

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