Rotary matching of edge features for leaf recognition

Authors:
Chih-Ying Gwo;Chia-Hung Wei;Yue Li
Affiliations:
Department of Information Management, Chien Hsin University of Science and Technology, 229 Chien-Hsin Road, Taoyuan 320, Taiwan;Department of Information Management, Chien Hsin University of Science and Technology, 229 Chien-Hsin Road, Taoyuan 320, Taiwan;College of Software, Nankai University, 94 Weijin Road, Nankai District, 300071, China
Venue:
Computers and Electronics in Agriculture
Year:
2013

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Abstract

With advances in cloud computing technology, handheld computers and smartphones can now perform plant recognition by taking a photograph of a plant. This study proposes novel features to describe leaf edge variation. The Bayes theorem is used to calculate the maximal matching score for rotary matching. The Viterbi training algorithm is then applied to find the model parameters of rotary matching. The experimental results show that the top one of 13-tuple reaches 94.4% and the first two can also achieve 100% in the test set. The results have verified that the proposed features are invariant to translation, rotation and size.