NDLI: Recognition of pests based on compressive sensing theory

Content Provider	IEEE Xplore Digital Library
Author	Antai Han Hui Peng Jianfeng Li Jianqiang Han Xiaohua Guo
Copyright Year	2011
Description	Author affiliation: Fair Friend Institute of Electromechanics, Hangzhou Vocational and Technical College, Hangzhou 310018, China (Xiaohua Guo) \|\| Institute of Electrical Engineering and Electronic, Technology, China Jiliang University, Hangzhou 310018, China (Antai Han; Hui Peng; Jianfeng Li; Jianqiang Han)
Abstract	In order to improve the performance of the existing recognition methods of pests, the limitations of these methods are analyzed in this paper. Based on the analysis, the novel recognition method of pests by using compressive sensing theory is presented in this paper. In the proposed method, a large number of representative training samples of pests are used to construct the training samples matrix, then the sparse decomposition representation of the testing samples of pests is obtained by solving the L1-norm optimization problem, which contains distinct class information and could be used for the different species of pests recognition directly. The 12 species of stored-grain pests and the 110 species of common pests are separately recognized by the proposed method. The experimental results prove that the application of compressive sensing theory in the recognition of pests is practical and feasible.
Starting Page	263
Ending Page	266
File Size	524959
Page Count	4
File Format	PDF
ISBN	9781612844855
e-ISBN	9781612844862
DOI	10.1109/ICCSN.2011.6014437
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	pests feature parameters Matching pursuit algorithms recognition Vectors Sparse matrices Approximation methods Optimization Training sparse decomposition recognition precision compressive sensing Testing
Content Type	Text
Resource Type	Article

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