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首页> 外文会议>International conference on machine vision >Automated Grain Extraction and Classification by Combining Improved Region Growing Segmentation and Shape Descriptors in Electromagnetic Mill Classification System
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Automated Grain Extraction and Classification by Combining Improved Region Growing Segmentation and Shape Descriptors in Electromagnetic Mill Classification System

机译：结合改进的区域增长分割和形状特征在电磁磨分类系统中自动进行谷物的提取和分类

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摘要

In this paper, the automatic method of grain detection and classification has been presented. As input, it uses a single digital image obtained from milling process of the copper ore with an high-quality digital camera. The grinding process is an extremely energy and cost consuming process, thus granularity evaluation process should be performed with high efficiency and time consumption. The method proposed in this paper is based on the three-stage image processing. First, using Seeded Region Growing (SRG) segmentation with proposed adaptive thresholding based on the calculation of Relative Standard Deviation (RSD) all grains are detected. In the next step results of the detection are improved using information about the shape of the detected grains using distance map. Finally, each grain in the sample is classified into one of the predefined granularity class. The quality of the proposed method has been obtained by using nominal granularity samples, also with a comparison to the other methods.
机译：本文提出了一种谷物自动检测与分类方法。作为输入，它使用通过高质量数码相机对铜矿石进行铣削处理而获得的单个数字图像。研磨过程是非常耗费能量和成本的过程，因此应该高效且耗时地进行粒度评估过程。本文提出的方法基于三阶段图像处理。首先，在基于相对标准偏差（RSD）的计算基础上，使用带有建议的自适应阈值的种子区域生长（SRG）分割，可以检测到所有谷物。在接下来的步骤中，使用有关通过距离图检测到的谷物形状的信息来改善检测结果。最后，将样品中的每种谷物分类为预定义的粒度类别之一。通过使用标称粒度样本以及与其他方法的比较，已经获得了所提出方法的质量。

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