NDLI: Sawtooth feature extraction of leaf edge based on support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Heng-Nian Qi Jian-Gang Yang
Copyright Year	2003
Description	Author affiliation: Inst. of Artificial Intelligence, Zhejiang Univ., China (Heng-Nian Qi; Jian-Gang Yang)
Abstract	The focus of Computer-Aided Plant-Identification (CAPI) is the stable features extraction of plant, such as sawtooth number of leaf edge for some species of plants. Because the shape of the sawteeth varies greatly, they cannot be depicted in rigid mathematic method. However, a trained SVM (Support Vector Machine) with good adaptability can be applied to classify sawtooth and nonsawtooth samples. The samples can be obtained by a rectangular sample window sliding along the edge of the leaf, and then be rotated to a standard pose for decreasing the complexity of identification. By avoiding repeated sampling and counting of the same sawtooth, the algorithm presented in the paper accomplishes automatic counting of the sawtooth number. The results of the experiment show that the SVM-based method works well.
Sponsorship	IEEE Systems Man and Cybernetics Tech. Committee on Cybernetics, Hebei Univ. Northwestern Polytechnical Univ
Starting Page	3039
Ending Page	3044
File Size	320667
Page Count	6
File Format	PDF
ISBN	0780381319
DOI	10.1109/ICMLC.2003.1260099
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Support vector machines Face recognition Handwriting recognition Image recognition Support vector machine classification Shape Neural networks Artificial intelligence Forestry
Content Type	Text
Resource Type	Article

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