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Artificial Vision to assure Coffee-Excelso Beans quality

机译:人工视觉确保优质的Excelso咖啡豆

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This paper studies the possibility of classifying coffee beans by using their features of color, shape and size. The process of acquiring the images were done in controlled lighting conditions.Based on segmented images, color, shape and size provide information about the physical alteration in green coffee beans, during the growing and drying process that affect the flavor and taste of the drink by Developing a computer program to sort coffee beans by typesIn the case of color images are analyzed in RGB space, which were preprocessor in order to reduce noise and separate from the background and enhance its features. The location was used for an algorithm for identifying and setting contours ellipse, an algorithm Mahalanobis distance classifier, an algorithm called Flood of growth, for the segmentation of those defective grains.The emergence of methods that take advantage of technological advances is an option whose benefits encourage the study of new possibilities. The classification of coffee beans throughout the analysis of images is a very promising because it is a minimally invasive method and the exposure of the grains are not visible to look deteriorates significantly.Many farmers sort their coffee beans by hand but only a few thousand dollars invested in a automatic sorting optical machine such us Sortex or Xeltron. In this paper we develop algorithms based on image processing techniques for the classification of defective beans
机译:本文研究了通过使用咖啡豆的颜色,形状和大小对其进行分类的可能性。获取图像的过程是在受控的光照条件下完成的。 根据细分的图像,颜色,形状和大小可提供有关生咖啡豆在生长和干燥过程中的物理变化的信息,这些变化通过开发一种计算机程序来按类型对咖啡豆进行分类,从而影响饮料的风味和口感 在彩色图像的情况下,在RGB空间中进行了分析,这些图像经过预处理以减少噪声并与背景分离并增强其功能。该位置用于识别和设置轮廓椭圆的算法,马氏距离分类器算法,称为泛洪的算法,用于分割那些缺陷晶粒。 利用技术进步的方法的出现是一种选择,其好处鼓励了对新可能性的研究。在整个图像分析过程中,对咖啡豆进行分类是非常有前途的,因为它是一种微创方法,并且谷物的暴露不可见,外观也明显恶化。 许多农民用手工对咖啡豆进行分拣,但是在诸如Sortex或Xeltron这样的自动分拣光学机器上仅投资了几千美元。在本文中,我们开发了基于图像处理技术的算法,用于缺陷豆的分类

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