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Research on Key Technology of Diamond Particle Detection Based on Machine Vision

机译:基于机器视觉的金刚石颗粒检测关键技术研究

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Most traditional methods for detecting the quality of tiny particles are manually detected by measurement tools. Manual detection has the limitations of low efficiency, high false detection rate and low detection accuracy, and is gradually replaced by non-contact measurement. In this paper, a new denoising algorithm based on median and mean filtering is proposed in diamond particle image analysis. Dynamic threshold segmentation is combined with the Canny algorithm for edge extraction. In the edge extraction process of the particle edge, it will lead to non-closed problems. A contour edge location method based on Hough transform is proposed. Thereby, parameters such as particle size, roundness and ellipticity of the diamond particles are measured.
机译:用于检测微小颗粒质量的大多数传统方法是通过测量工具手动检测的。手动检测具有效率低,误检率高,检测精度低的局限性,并逐渐被非接触式测量所取代。本文提出了一种新的基于中值和均值滤波的去噪算法。动态阈值分割与Canny算法结合在一起用于边缘提取。在粒子边缘的边缘提取过程中,会导致非封闭问题。提出了一种基于霍夫变换的轮廓边缘定位方法。从而,测量诸如金刚石颗粒的粒径,圆度和椭圆度的参数。

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