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Determining Gravimetric Bark Content in Cotton with Machine Vision

机译:用机器视觉测定棉花中的树皮重量

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摘要

A method is needed to accurately and rapidly determine the gravimetric bark content of a cotton sample. Gravimetric bark content represents the percent bark mass throughout the volume of a cotton sample. The current method for measuring gravimetric bark content is a labor intensive, lengthy process. Machine vision, on the other hand, is a fast, inexpensive method to measure this bulk cotton property. Ten acquired images of surfaces throughout each sample are used. Classical digital image processing techniques isolate foreign matter regions in monochrome video images. Geometric properties (area and perimeter) are used to identify which foreign matter is bark and to predict the gravimetric bark content in forty-eight cotton samples with varying bark and total foreign matter content. We suggest a model with six features and intercept, which has an estimated error of 0.46% bark mass.
机译:需要一种方法来准确和快速地确定棉花样品的重量树皮含量。重量树皮含量代表整个棉花样品中树皮的质量百分比。当前用于测量重量树皮含量的方法是费力的,漫长的过程。另一方面,机器视觉是一种快速,廉价的方法来测量这种棉的散装特性。在每个样品中使用十张采集的表面图像。经典的数字图像处理技术隔离单色视频图像中的异物区域。使用几何特性(面积和周长)来确定哪个异物是树皮,并预测48个棉样中的树皮重量含量,其中树皮和总异物含量都不同。我们建议使用具有六个特征和截距的模型,该模型的估计误差为0.46%树皮质量。

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