Rotary matching of edge features for leaf recognition

被引：14

作者：

Gwo, Chih-Ying ^{[1
]}

Wei, Chia-Hung ^{[1
]}

Li, Yue ^{[2
]}

机构：

[1] Chien Hsin Univ Sci & Technol, Dept Informat Management, Tao Yuan 320, Taiwan

[2] Nankai Univ, Coll Software, Tianjin 300071, Peoples R China

来源：

COMPUTERS AND ELECTRONICS IN AGRICULTURE | 2013年 / 91卷

关键词：

Leaf recognition; Bayes theorem; Pattern recognition; Viterbi training algorithm; SHAPE; CLASSIFICATION;

D O I：

10.1016/j.compag.2012.12.005

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

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. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：124 / 134

页数：11

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