Abstract
The Local Binary Pattern (LBP) and its variants is powerful in capturing image features and computational simplicity, However LBP’s sensitivity to noise, particularly in near-uniform image regions has stimulated many transformations of LBP to improve the ability of feature description. The Local Ternary Pattern (LTP) extends the conventional LBP to ternary codes and makes a significant improvement. LTP is more resistant to noise, but no longer strictly invariant to gray-level transformations. In this paper, by adopting the Average Local Gray Level (ALG) to take place of the traditional gray value of the center pixel and taking an auto-adaptive strategy on the selection of the threshold, we propose the Enhanced Local Ternary Pattern (ELTP) to improve the performance of LTP and implement an android application to recognize plant-leaf image and identify the species of the plant.
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Acknowledgments
This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61373098 and 61272333, China Postdoctoral Science Foundation Grant, Nos. 2014M561513, and partly supported by the National High-Tech R&D Program (863) (2014AA021502 & 2015AA020101), and the grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120072110040), and the grant from the Outstanding Innovative Talent Program Foundation of Henan Province, No. 134200510025.
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Xu, GS. et al. (2015). Implementation of Plant Leaf Recognition System on ARM Tablet Based on Local Ternary Pattern. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_15
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DOI: https://doi.org/10.1007/978-3-319-22186-1_15
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