TY - SER
KW - Application programs
KW - Boltzmann
KW - Cluster analysis
KW - Clustering method
KW - Clustering methods
KW - Computer Vision
KW - Design and implementations
KW - Employment
KW - Ethnic culture
KW - Ethnic groups
KW - Ethnic Minorities
KW - Hand-painted thangka
KW - Intangible cultural heritage
KW - Intangible cultural heritages
KW - LBP algorithm
KW - MVC
KW - Machine-printed thangka
KW - MySQL database
KW - Online sales
KW - Palmprint recognition
KW - processing
KW - Processing method
KW - Qinghai Tibet plateau
KW - Restricted Boltzmann algorithm
KW - Restricted boltzmann algorithm
KW - Sales
KW - Sales management
KW - Textures
KW - Tibetans
KW - Vision processing
AU - D. Zhao
AU - C. Pan
AB - Thangka is a unique painting art form in Tibetan culture. As Thangka was listed as the first batch of national intangible cultural heritage, it has received more and more attention. At the same time, many printed thangkas appeared on the market, and some merchants mixed printed thangkas with hand-painted thangkas and sold them at high prices. How to distinguish between hand-painted thangkas and machine-printed thangkas, and to guide the inheritance and protection of hand-painted thangkas is an important issue. Based on the analysis of the characteristics of thangkas, combined with related computer vision processing methods, this paper conducts method research and model construction, uses the LBP algorithm to analyze the texture of the canvas, identifies the canvas of the thangka based on the training results, and uses restricted Boltzmann s light transmittance analysis of thangkas, and the use of clustering method to analyze the gold line of thangkas and other methods combined, proposed a feasible method of identifying hand-painted thangkas and machine-printed thangkas. This method should play a significant role in the protection and inheritance of the country. This specification is set for scientific papers published in “Computer Applications and Software”. The author is requested to read and implement one by one, if it does not meet the requirements, it will affect the publication of the article.
C1 - 3rd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2021
DO - 10.1007/978-3-030-90199-8_20
N1 - Journal Abbreviation: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng. Pages: 213 Publication Title: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.
N2 - Thangka is a unique painting art form in Tibetan culture. As Thangka was listed as the first batch of national intangible cultural heritage, it has received more and more attention. At the same time, many printed thangkas appeared on the market, and some merchants mixed printed thangkas with hand-painted thangkas and sold them at high prices. How to distinguish between hand-painted thangkas and machine-printed thangkas, and to guide the inheritance and protection of hand-painted thangkas is an important issue. Based on the analysis of the characteristics of thangkas, combined with related computer vision processing methods, this paper conducts method research and model construction, uses the LBP algorithm to analyze the texture of the canvas, identifies the canvas of the thangka based on the training results, and uses restricted Boltzmann s light transmittance analysis of thangkas, and the use of clustering method to analyze the gold line of thangkas and other methods combined, proposed a feasible method of identifying hand-painted thangkas and machine-printed thangkas. This method should play a significant role in the protection and inheritance of the country. This specification is set for scientific papers published in “Computer Applications and Software”. The author is requested to read and implement one by one, if it does not meet the requirements, it will affect the publication of the article.
PB - Springer Science and Business Media Deutschland GmbH
SN - 18678211 (ISSN); 9783030901981 (ISBN)
TI - Research on the Identification of Hand-Painted Thangka and Printed Thangka Based on Computer Vision Processing Method
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120033633&doi=10.1007%2f978-3-030-90199-8_21&partnerID=40&md5=e7491584cba163b61076735de47f7a52
VL - 397 LNICST
ER -