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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lei Zhang Jun Kong Xiaoyun Zeng Jiayue Ren |
| Copyright Year | 2008 |
| Description | Author affiliation: Aviation Univ. of Air Force, Changchun (Jiayue Ren) || Comput. Sch., Northeast Normal Univ., Changchun (Jun Kong) || Network Inf. Center, Northeast Normal Univ., Changchun (Lei Zhang) || Yangzhou Polytech. Coll., Yangzhou (Xiaoyun Zeng) |
| Abstract | Computer-aided plant species identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plant. This paper presents a new method for plant species identification using leaf image. It focuses on the stable features extraction of leaf, such as the geometrical features of shape and the texture features of venation. The 2-D moment invariants, Wavelet statistical features are used to extract leaf information. Self-organizing feature map (SOM) neural network has the advantages of simple structure, ordered mapping topology and low complexity of learning. It is suitable for many complex problems such as multi-class pattern recognition, high dimension input vector and large quantity training data. So this paper use SOM neural network to identify the plant species. The experimental results illustrate the effectiveness of this method. |
| Starting Page | 90 |
| Ending Page | 94 |
| File Size | 649500 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769533049 |
| DOI | 10.1109/ICNC.2008.253 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Shape Military computing Wavelet statistical features Artificial neural networks Educational institutions plant species identification Data mining Research and development Network topology Neural networks SOM neural network Feature extraction Computer networks |
| Content Type | Text |
| Resource Type | Article |
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