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Development of a Gemstone Type Identification System Based on HSV Space Colour Using an Artificial Neural Network Back Propagation Algorithm

机译:利用人工神经网络反向传播算法的基于HSV空间颜色的宝石型识别系统的开发

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A gemstone is a mineral stone that be formed from the result of geological processes and has a hardness above 7 Mohs. Nowadays, gemstones have become famous in Indonesian society. Many facts concerning the business of gemstone rings, including encouragement by the the central government for the gemstone souvenirs that are given to State guests by the President of Indonesia, gemstone contests and gemstone exhibitions have all contributed to pique the interest of researchers in the subject. This current research is undertaken to create a system that is able to identify three kinds of gemstone, namely, Ruby, Sapphire and Emerald using the Hue Saturation Value (HSV) colour space, image processing techniques and an Artificial Neural Network (ANN) back propagation algorithm by learning from examples. The hue from the HSV colour space of a gemstone will be used by the system for training. In the image processing, the system will crop, resize, convert from RGB to HSV, obtain a Hue colour and extract the colour as a 30×3 matrix. The extracted result will be used to train an ANN consisting of three input layers, three hidden layers and one output layer with targets that have been pre-determined. The results of the tests showed a degree of accuracy of 90.66% with 5 times of training and 25 times of testing on any type of gemstone. The result shows that using Artificial Neural Network Back Propagation in identification gemstone types is success, because the accuracy system has a highest percentage.
机译:宝石是一种矿物石材,由地质过程的结果形成,并且具有高于7莫赫的硬度。如今,宝石在印度尼西亚社会中着名。关于Gemstone戒指的许多事实,包括由印度尼西亚总统,Gemstone比赛和宝石展览会给州嘉宾赋予州客人的高级政府的鼓励,这都有促进了对象研究人员的利益。该目前的研究是使用Hue饱和值(HSV)颜色空间,图像处理技术和人工神经网络(ANN)回到传播,创建一个能够识别三种宝石,即Ruby,Sapphire和祖母绿的系统通过示例学习算法。来自宝石的HSV颜色空间的色调将由系统用于培训。在图像处理中,系统将裁剪,调整大小,从RGB转换为HSV,获取色调颜色并将颜色提取为30×3矩阵。提取的结果将用于训练由三个输入层,三个隐藏层和一个输出层组成的ANN,其中具有预先确定的目标。测试结果显示,精度为90.66%,培训5倍,任何类型的宝石测试25倍。结果表明,在识别宝石类型中使用人工神经网络反向传播是成功的,因为精度系统具有最高百分比。

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