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http://dx.doi.org/10.7472/jksii.2018.19.1.97

Design and Implementation of Luo-kuan Recognition Application  

Kim, Han-Syel (Department of Computer Software Engineering, Soonchunhyang University)
Seo, Kwi-Bin (Department of Computer Science, Soonchunhyang University)
Kang, Mingoo (Department of IT Contents, Hanshin University)
Ryu, Gee Soo (Department of Chinese Culture & Art Studies, Hanshin University)
Hong, Min (Department of Computer Software Engineering, Soonchunhyang University)
Publication Information
Journal of Internet Computing and Services / v.19, no.1, 2018 , pp. 97-103 More about this Journal
Abstract
In oriental paintings, there is Luo-kuan that expressed in a single picture by compressing the artist's information. Such Luo-kuan includes various information such as the title of the work or the name of the artist. Therefore, information about Luo-kuan is considered important to those who collect or enjoy oriental paintings. However, most of the letters in the Luo-kuan are difficult kanji, kanzai, or various shapes, so it is difficult for the ordinary people to interpret. In this paper, we developed an Luo-kuan search application to easily check the information of the Luo-kuan. The application uses a search algorithm that analyzes the captured Luo-kuan image and sends it to the server to output information about the Luo-kuan candidates that are most similar to the Luo-kuan images taken from the database in the server. We also compared and analyzed the accuracy of the algorithm based on 170 Luo-kuan data in order to find out the ranking of the Luo-kuan that matched the Luo-kuan among the candidates. Accuracy Analysis Experimental Results The accuracy of the search algorithm of this application is confirmed to be about 90%, and it is anticipated that it will be possible to develop a platform to automatically analyze and search images in a big data environment by supplementing the optimizing algorithm and multi-threading algorithm.
Keywords
Luo-kuan(落 款 ); Mobile Application; Image processing;
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1 Sanya Patel, Aliya Sayyed, P.P Bastawade, "Smart Attendance Application Using Android and PHP", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 6, Issue 3, 2017 https://doi.org/10.17148/ijarcce.2017.6351   DOI
2 Shoki Fukuda, Shun Kurihara, Shintaro Hamanaka, Masato Oguchi, Saneyasu Yamaguchi, "Accelerated test for applications with client application and server software", IMCOM '17 Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, No.100, 2017 http://dx.doi.org/10.1145/3022227.3022326   DOI
3 HAN Lu, LI Zu-shu, CHEN Dong-yi, "Object detection module based on implementation of Java and OpenCV", Journal of Computer Applications, pp. 733-775, 2008 https://doi.org/10.3724/sp.j.1087.2008.00773   DOI
4 Pavel JETENSKY, "Using Open Source OpenCV Library for practical coureses of Computer Vision", Journal of Technology and Information Education, Vol. 5, Issue 1, 2013 https://doi.org/10.5507/jtie.2013.017   DOI
5 R. Manoharan, S. Chandrakala, "Android OpenCV based effective driver fatigue and distraction monitoring system", Computing and Communications Technologies (ICCCT), 2015 International Conference on, 2015 https://doi.org/10.1109/iccct2.2015.7292757   DOI
6 OpenCV, "Feature Matching", http://docs.opencv.org/trunk/dc/dc3/tutorial_py_matcher.html
7 Wikipedia, "Feature detection(computer vision)", https://en.wikipedia.org/wiki/Feature_detection_(computer_vision)
8 Guobo Xie, Wen Lu, "Image Edge Detection Based On Opencv", International Journal of Electronics and Electrical Engineering, Vol.1, No.2, 2013 https://doi.org/10.12720/ijeee.1.2.104-106   DOI
9 Wikipedia, "Speeded up robust features(SURF)", https://en.wikipedia.org/wiki/Speeded_up_robust_features
10 S. Li "The Application of Face Recognition Based on OpenCV", Advanced Materials Research, Vols. 403-408, pp. 2350-2353, 2012 https://doi.org/10.4028/www.scientific.net/amr.403-408.2350   DOI
11 Kim, Gihong, Chong, Kyusoo, Youn, Junhee, "Automatic Recognition of Direction Information in Road Sign Image Using OpenCV", Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Volume 31, Issue 4, pp. 293-300, 2013. http://dx.doi.org/10.7848/ksgpc.2013.31.4.293   DOI
12 Ye Zhao, Richang Hong, Jianguo Jiang, Jing Wen, Hengheng Zhang, "Image matching by fast random sample consensus", ICIMCS '13 Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service, pp.159-162, 2013. http://dx.doi.org/10.1145/2499788.2499852   DOI
13 Raj Kumar Gupta, Alex Yong-Sang Chia, Deepu Rajan, Ee Sin Ng, Huang Zhiyoung, "Image colorization using similar images", MM '12 Proceedings of the 20th ACM international conference on Multimedia, pp. 369-378, 2012. http://dx.doi.org/10.1145/2393347.2393402   DOI
14 Yaron Lipman, Stav Yagev, Roi Poranne, David W. Jacobs, Ronen Basri, "Feature Matching with Bounded Distortion", ACM Transactions on Graphics (TOG), Volume 33, Issue 3, 2014. http://dx.doi.org/10.1145/2602142   DOI