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The Scientific World Journal
Volume 2014 (2014), Article ID 373254, 10 pages
Research Article

Completed Local Ternary Pattern for Rotation Invariant Texture Classification

School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, 14300 Penang, Malaysia

Received 20 December 2013; Accepted 11 February 2014; Published 7 April 2014

Academic Editors: G. C. Gini and L. Li

Copyright © 2014 Taha H. Rassem and Bee Ee Khoo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [4 citations]

The following is the list of published articles that have cited the current article.

  • Jin Tae Kwak, Sheng Xu, and Bradford J. Wood, “Efficient Data Mining for Local Binary Pattern in Texture Image Analysis,” Expert Systems with Applications, 2015. View at Publisher · View at Google Scholar
  • Harith Al-Sahaf, Ausama Al-Sahaf, Bing Xue, Mark Johnston, and Mengjie Zhang, “Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2016. View at Publisher · View at Google Scholar
  • Taha H. Rassem, Nasrin M. Makbol, and Sam Yin Yee, “Face recognition using completed local ternary pattern (CLTP) texture descriptor,” International Journal of Electrical and Computer Engineering, vol. 7, no. 3, pp. 1594–1601, 2017. View at Publisher · View at Google Scholar
  • Rafidah Muhamad, Hairudin Abdul Majid, Ghazali Sulong, Shahreen Kasim, Azurah Abu Samah, and Mohd Saberi Mohamad, “Review on local binary patterns variants as texture descriptors for copy-move forgery detection,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1678–1684, 2017. View at Publisher · View at Google Scholar