Recognition of whole and deformed plant leaves using statistical shape features and neuro-fuzzy classifier

Jyotismita Chaki, R. Parekh, S. Bhattacharya
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引用次数: 19

Abstract

This paper proposes a methodology for recognition of plant species by using a set of statistical features obtained from digital leaf images. As the features are sensitive to geometric transformations of the leaf image, a pre processing step is initially performed to make the features invariant to transformations like translation, rotation and scaling. Images are classified to 32 pre-defined classes using a Neuro fuzzy classifier. Comparisons are also done with Neural Network and k-Nearest Neighbor classifiers. Recognizing the fact that leaves are fragile and prone to deformations due to various environmental and biological factors, the basic technique is subsequently extended to address recognition of leaves with small deformations. Experimentations using 640 leaf images varying in shape, size, orientations and deformations demonstrate that the technique produces acceptable recognition rates.
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利用统计形状特征和神经模糊分类器识别整片和变形的植物叶片
本文提出了一种利用从数字叶片图像中获得的一组统计特征来识别植物物种的方法。由于特征对叶片图像的几何变换敏感,因此首先进行预处理步骤,使特征对平移、旋转和缩放等变换不变性。使用神经模糊分类器将图像分类为32个预定义的类。神经网络和k近邻分类器也进行了比较。认识到由于各种环境和生物因素,叶子是脆弱的,容易变形,随后将基本技术扩展到处理小变形叶子的识别。使用640张不同形状、大小、方向和变形的叶子图像进行的实验表明,该技术产生了可接受的识别率。
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