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

机译:使用统计形状特征和神经模糊分类器识别完整和变形的植物叶片

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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.
机译:本文提出了一种利用从数字叶片图像获得的统计特征来识别植物种类的方法。由于特征对叶子图像的几何变换敏感,因此首先执行预处理步骤以使特征对于变换(例如平移,旋转和缩放)不变。使用神经模糊分类器将图像分类为32个预定义的类。还使用神经网络和k最近邻分类器进行了比较。认识到叶片易碎,并且由于各种环境和生物因素而易于变形的事实,随后扩展了基本技术,以解决具有较小变形的叶片的识别。使用形状,大小,方向和变形变化的640张叶片图像进行的实验表明,该技术可产生可接受的识别率。

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