A Neural Network Based Approach For Recognition And Classification Of Color Features For Vegetables

S. Shanmugam, J. Devi
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Abstract

The idea in this work is combining the GLOH algorithm and PCA to detect and classify the color features of vegetables (or) fruits. The detection and classification of color features of vegetables (or) fruits is an identified phase of research in agriculture. In technical way, image processing defines the processing of signals, gets input as an image, photo or video frame; and the output of this process can be an image or image related parameters. The aim of the paper is detecting and classifying the vegetables / fruits according to its color. It contains three parts, first two parts uses algorithms such as GLOH and SIFT-PCA, used for feature extraction and its reduction and key point extraction and dimensionality reduction. The third part of this work uses PCA to classifies the vegetables. The final result of the work is achieved by integrating the above three parts.
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基于神经网络的蔬菜颜色特征识别与分类方法
本工作的思路是将GLOH算法与PCA相结合,对蔬菜(或)水果的颜色特征进行检测和分类。蔬菜(或)水果颜色特征的检测和分类是农业研究的一个重要阶段。在技术上,图像处理定义了对信号的处理,以图像、照片或视频帧的形式输入;而这个过程的输出可以是图像或与图像相关的参数。本文的目的是根据蔬菜/水果的颜色进行检测和分类。它包括三个部分,前两个部分使用GLOH和SIFT-PCA等算法,用于特征提取和约简,关键点提取和降维。第三部分采用主成分分析法对蔬菜进行分类。最后的工作结果是将以上三部分综合起来得出的。
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