Measurement of sparse scatter particulates based on computer vision

ZHANG Xue-jun; ZUO Chun-cheng; WEN Wei-li; ZHU Meng-yi

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Measurement of sparse scatter particulates based on computer vision

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- Measurement of sparse scatter particulates based on computer vision
- Optics and Precision Engineering Vol. 15, Issue 4, Pages: 611-614(2007)
- 作者机构：
  
  吉林大学机械科学与工程学院, 吉林长春 130025
- 作者简介：
- 基金信息：
- DOI：
  CLC： TB92;TP242.6
- Received：13 December 2006，
  
  Revised：18 February 2007，
  
  Published Online：30 April 2007，
  
  Published：30 April 2007
- 稿件说明：
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ZHANG Xue-jun, ZUO Chun-cheng, WEN Wei-li, et al. Measurement of sparse scatter particulates based on computer vision[J]. Optics and precision engineering, 2007, 15(4): 611-614.
DOI：

ZHANG Xue-jun, ZUO Chun-cheng, WEN Wei-li, et al. Measurement of sparse scatter particulates based on computer vision[J]. Optics and precision engineering, 2007, 15(4): 611-614. DOI：

摘要

针对场流分离后

微粒处理时存在尺寸不直观等问题

提出了基于图像处理技术检测微粒尺寸的方法。首先用CCD并配合图像处理软件采集微粒图像并进行预处理

然后用阈值化方法分割图像。在测量尺寸时

以小麦粉作为被测量样本

利用动态聚类处理

得出微粒16个、最大半径1.600 μm、最小半径1.000 μm、平均半径1.187 5 μm、半径方差0.661 4等。试验表明

本文提出的检测微粒尺寸参数的方法方便可行。

Abstract

A method to measure micro particulates based on image processing is presented. The particulate image is collected and pre-processed by CCD combined with a image-processing software system

then segmented by threshold mothod. By using flour as specimens and utilizing the dynamic clustering method coded by C++

the 16 particulates with the maximum radius of 1.600 μm

minimum radius of 1.000 μm

average radius of 1.187 5 μm and standard deviation of radius of 0.661 4 have been obtained. The experiment results indicate that the presented method is effective and feasible.

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Related Author

JIANG Deng-biao1

LI Bo1

CHEN Qi-mei1

GUO Qi1

MA Li-ya1

REN Jian-wei4

Related Institution

Zhenjiang High-Tech Research Institute of Nanjing University

School of Electronic Science and Engineering，Nanjing University

中国科学院长春光学精密机械与物理研究所

中国科学院大学

中国科学院特殊环境功能材料与器件重点实验室

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