2D IMAGE BASED SIEVING FOR PARTICLE AGGREGATE GRADATION  

作　　者：Chen Ken John Zaniewski Zhao Pan Yang Ren＇er 

机构地区：[1]College of Information Science and Engineering, Ningbo University, Ningbo 315211, China [2]College of Engineering, West Virginia University, WV 26506-6106, USA

出　　处：《Journal of Electronics(China)》2008年第2期277-282,共6页电子科学学刊（英文版）

基　　金：Ningbo Natural Science Foundation (No. 2006A610016);Foundation of National Education Ministry for Returned Overseas Students & Scholars (SRF for ROCS, SEM. No.2006699).

摘　　要：Acquiring the size gradation for particle aggregates is a common practice in the granule related industry,and mechanical sieving or screening has been the normal method. Among many drawbacks of this conventional means,the major ones are time-consuming,labor-intensive,and being unable to provide real-time feedback for process control. In this letter,an optical sieving approach is introduced. The two-dimensional images are used to develop methods for inferring particle volume and sieving behavior for gradation purposes. And a combination of deterministic and probabilistic methods is described to predict the sieving behaviors of the particles and to construct the gradation curves for the aggregate sample. Comparison of the optical sieving with standard mechanical sieving shows good correlation.Acquiring the size gradation for particle aggregates is a common practice in the granule related industry, and mechanical sieving or screening has been the normal method. Among many drawbacks of this conventional means, the major ones are time-consuming, labor-intensive, and being unable to provide real-time feedback for process control. In this letter, an optical sieving approach is introduced. The two-dimensional images are used to develop methods for inferring particle volume and sieving behavior for gradation purposes. And a combination of deterministic and probabilistic methods is described to predict the sieving behaviors of the particles and to construct the gradation curves for the aggregate sample. Comparison of the optical sieving with standard mechanical sieving shows good correlation.

关 键 词：Image processing Machine vision Particle images Particle gradation Particle sieving 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]