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AUTOMATIC SEEDS RECOGNITION BY SIZE, FORM AND TEXTURE FEATURES

机译:自动种子尺寸,形式和纹理特征识别

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This work deals with an automatic seeds analysis system based on pattern recognition methods. However, this paper emphasizes only the pattern recognition aspects of the problem and, for our tests, four hundred samples of each of four species of seeds, namely corn, oat, barley and lentil are considered The recognition procedure is, firstly, made on the basis of shape features and texture features, separately. Both of theses methods have given good results with a small confusion rate. In order to increase the recognition rate, the shape and texture features are used all together leading then to results considerably improved After images acquisition and their pre-processing, the general process includes the features space reduction using the Principal Component and clustering operation based the k-means algorithm The decision phase is based on the nearest Euclidean distance rule between the feature vector of an unknown seed and the average feature vector of each cluster.
机译:这项工作涉及基于模式识别方法的自动种子分析系统。但是,本文仅重点出现问题的模式识别方面,并且对于我们的测试,四种种子中的每一个种子,即玉米,燕麦,大麦和扁豆的四百个样本被认为是识别程序,首先是在形状特征和纹理特征的基础,分别。两种方法都以小的混乱速度给出了良好的结果。为了提高识别率,形状和纹理特征在一起,在图像获取和预处理之后,它们的形状和纹理特征得到了相当改善,但是一般过程包括使用基于k的主组件和聚类操作的特征空间减少 - 策略算法决策阶段基于未知种子的特征向量和每个群集的平均特征向量之间的最近的欧几里德距离规则。

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