Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/71418
Title: Extraction of leaf vein features based on artificial neural network - studies on the living plant identification Ⅰ
Other Titles: 基于人工神經網絡的葉脈信息提取 - 植物活體機器識別研究Ⅰ
Authors: Fu, H 
Chi, Z 
Chang, J
Fu, CX
Keywords: Vein extraction
Artificial neural networks
Plant identification
Local contrast
Issue Date: 2004
Publisher: 中國科學院植物研究所, 中國植物學會
Source: 植物學報 (Chinese Bulletin of Botany), 2004, v. 21, no. 4, p. 429-36 How to cite?
Journal: 植物學報 (Chinese Bulletin of Botany) 
Abstract: 葉片的識別是識別植物的重要組成部分,特別在野外識別植物活體尤其重要。葉脈的脈序是植物的內在特征,包含有重要的遺傳信息。但由于葉脈本身的多樣性,利用單一特征的圖像處理方法難以有效地提取葉脈。為了充分利用圖像的信息,本文提出了一種基于人工神經網絡的葉脈提取方法。該方法利用邊緣梯度、局部對比度和鄰域統計特征等10個參數來描述像素的鄰域特征,并將其作為神經網絡的輸入層。實驗結果表明,與傳統方法相比,經過訓練的神經網絡能夠更準確地提取葉脈圖像,為進一步的葉片識別打下了良好的基礎。
Leaf recognition is an important step for plant computerized identification, especially for fieldliving plants. Previous researches were mainly focused on leaf recognition by utilizing the peripheralcontour of the leaf while ignoring the leaf venation that actually contains important genetic information.Conventional thresholding-based methods cannot extract the information accurately due to high diversityof leaf veins. In this paper, an approach based on artificial neural network learning is proposed to extractleaf venation. Ten features including edge gradients, local contrast and statistical features are extractedfrom a window centered at the image pixel and used to train a neural network classifier. Compared withconventional thresholding-based methods, the trained neural network is capable of extracting more accu-rate modality of leaf venation for subsequent leaf recognition.
URI: http://hdl.handle.net/10397/71418
ISSN: 1674-3466
Rights: © 2004 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。
© 2004 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research purposes.
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