Education, Science, Technology, Innovation and Life
Open Access
Sign In

Ceramic Art Based on Digital Technology Image Processing

Download as PDF

DOI: 10.23977/jipta.2024.070105 | Downloads: 7 | Views: 167

Author(s)

Ziyi Fang 1, Ruichen Niu 2, Tongyu Cui 1, Yudong Fang 2

Affiliation(s)

1 Beijing Institute of Fashion Technology, Beijing, 100000, China
2 Beijing Forestry University, Beijing, 100000, China

Corresponding Author

Yudong Fang

ABSTRACT

Ceramics first emerged in ancient China, and the development of ceramic art has a long history. In today's digital age, how to use the power of science and technology to conduct research on ceramic art is a problem worthy of attention. Digital Image Processing (DIP for short) is a method of using computer to perform graphics calculation, which has the characteristics of high efficiency and intelligence. It aims to use DIP technology to study ceramic art. In this regard, it has proposed to use convolutional neural network (CNN for short) to extract image features, identify and detect ceramic artworks, and has used mean filtering and median filtering to optimize the noise reduction of the identified images, so that the obtained image Higher quality. In the simulation test, it has selected 50 ceramic products for image recognition and detection, and has divided them into two groups for analysis. The results have showed that the inverse histograms of the two sets of images could reflect the defective parts of the ceramic products. Based on this, the lowest accuracy rate of CNN recognition was over 85%, and the highest accuracy rate was 94%. In the first group of images, the SNRs obtained by mean filtering and median filtering are the lowest of 7.7, 6.5, the highest are 8.7, 8.3, and the average SNR is about 8.0, 7.2; the SNR obtained by the two filtering methods of the second group of images is around 6.5 to 8.0, and the SNR of the mean filtering is slightly higher. Therefore, some practical results have been achieved in the research of ceramic art using DIP technology.

KEYWORDS

Ceramic Art, Digital Image Processing, CNN Image Recognition, Filtering and Noise Reduction Method

CITE THIS PAPER

Ziyi Fang, Ruichen Niu, Tongyu Cui, Yudong Fang, Ceramic Art Based on Digital Technology Image Processing. Journal of Image Processing Theory and Applications (2024) Vol. 7: 32-42. DOI: http://dx.doi.org/10.23977/jipta.2024.070105.

REFERENCES

[1] Kim H A. How to Increase Foreign visitors' Understanding and the Publicness of Translated Ceramic Art Terms in the Museum. The Jounal of Cultural Exchange, 2020, 9(1):273-290.
[2] Cho S N. A Study on the Extension of Traditional Formative Ceramic Art through Convergence of Design Mediums. The Korean Society of Science & Art, 2020, 38(5):463-475.
[3] Onuzulike O. "Traditional" Paradigm as Dividing Wall: Formal Analysis in the Study of African Ceramic Art Modernism. Critical Interventions, 2019, 13(2-3):158-179. 
[4] Scher S. Dressing the Other: Foreign Women in Moche Ceramic Art. West 86th, 2019, 26(2):188-213.
[5] Chrysafi A P, Athanasopoulos N, Siakavellas N J. Damage detection on composite materials with active thermography and digital image processing. International Journal of Thermal Sciences, 2017, 116(Complete):242-253.
[6] Prasad D S, Reddy B S. Digital image processing techniques for estimating power released from the corona discharges. IEEE Transactions on Dielectrics & Electrical Insulation, 2017, 24(1):75-82.
[7] Szabo K Z, A G J, B A P, kos Horvath c, Csaba Szabo d. Spatial analysis of ambient gamma dose equivalent rate data by means of digital image processing techniques. Journal of Environmental Radioactivity, 2017, 166(Pt 2):309-320.
[8] Loke K F, Rahman N A, Nazir R, Lewis R W. Study of Aqueous and Non-Aqueous Phase Liquid in Fractured Double-Porosity Soil Using Digital Image Processing. Geologia Croatica, 2018, 71(2):55-63.
[9] Antohe M E, Forna D A, Dascalu C G, Forna N C. Implications of Digital Image Processing in the Paraclinical Assessment of the Partially Edentated Patient. Revista de Chimie -Bucharest- Original Edition-, 2018, 69(2):521-524.
[10] Mazhir S N, Ali A H, Hadi F W, Mazher A N. Studying the effect of Dielectric Barrier Discharges on the Leukemia Blood Cells Using Digital Image Processing. IOSR Journal of Pharmacy and Biological Sciences, 2017, 12(2):06-12.
[11] Rui L, Liu X, Wang X. The Design of FPGA-based Digital Image Processing System and Research on Algorithms. International Journal of Future Generation Communication and Networking, 2017, 10(2):41-54.
[12] Wang Z, Guo J. Teaching and Practice Mode Reform in Digital Image Processing Curriculum. International Journal of Information and Education Technology, 2017, 7(7):557-560.
[13] Aljohani O, Abu-Siada A. Application of digital image processing to detect transformer bushing faults and oil degradation using FRA polar plot signature. IEEE Transactions on Dielectrics & Electrical Insulation, 2017, 24(1):428-436. 
[14] Nandihal P. Image Noise Type Identification in Microarray Images Using CNN-INC. International Journal of Advanced Trends in Computer Science and Engineering, 2020, 9(4):6279-6288.
[15] Liu R, Zhao Y, Wei S. Indexing of CNN Features for Large Scale Image Search. Pattern Recognition, 2018, 48(10):2983-2992.
[16] Wei Y, Sun X, Yang K. Hierarchical Semantic Image Matching using CNN Feature Pyramid. Computer Vision and Image Understanding, 2018, 169(apr.): 40-51.
[17] Ma Y, Liu Y, Xie Q. CNN-feature based automatic image annotation method. Multimedia Tools & Applications, 2019, 78(3):3767-3780.
[18] Yang Z, Lian J, Li S. Heterogeneous SPCNN and its application in image segmentation. Neurocomputing, 2018, 285(APR.12):196-203.
[19] Tang H, Ni R, Zhao Y. Median filtering detection of small-size image based on CNN. Journal of Visual Communication and Image Representation, 2018, 51(feb.): 162-168.
[20] Schwartz O, Gannot S, Habets E. Multispeaker LCMV Beamformer and Postfilter for Source Separation and Noise Reduction. IEEE/ACM Transactions on Audio Speech & Language Processing, 2017, 25(5):940-951.
[21] Shin D, Jeong S, Baek Y. A Balanced Feedforward Current-Sense Current-Compensation Active EMI Filter for Common-Mode Noise Reduction. IEEE Transactions on Electromagnetic Compatibility, 2020, 62(2):386-397.

Downloads: 1344
Visits: 103817

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.