基于人工神经网络(ANN)的腰果白整粒等级分类

IF 0.4 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Narendra Vg, Dasharathraj K. Shetty
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引用次数: 0

摘要

本文介绍了一种在给定图像中对腰果核封闭区域进行边界矩形拟合的算法。提出了一种自动计算顶点闭合形式解坐标的算法。它基于坐标几何并使用区域的边界点。该算法还利用最小二乘方法计算出了给定腰果核的方向。通过对腰果仁的形状特征进行提取,获得了较好的结果,证明这些特征可以更好地用于不同等级腰果仁的区分。采用人工神经网络(ANN)设计智能模型。采用Back-Propagation学习算法对模型进行训练和测试,分类准确率达到89.74%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
White whole (WW) grades cashew kernel’s classification using artificial neural network (ANN)
In this paper, we introduce an algorithm for the fitting of bounding rectangle to a closed region of cashew kernel in a given image. We propose an algorithm to automatically compute the coordinates of the vertices closed form solution. Which is based on coordinate geometry and uses the boundary points of regions. The algorithm also computes directions of major and minor axis using least-square approach to compute the orientation of the given cashew kernel. More promising results were obtained by extracting shape features of a cashew kernel, it is proved that these features may predominantly use to make the better distinction of cashew kernels of different grades. The intelligent model was designed using Artificial Neural Network (ANN). The model was trained and tested using Back-Propagation learning algorithm and obtained classification accuracy of 89.74%. 
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来源期刊
EMITTER-International Journal of Engineering Technology
EMITTER-International Journal of Engineering Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
自引率
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发文量
7
审稿时长
12 weeks
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