文章基本信息

标题：An Efficient of Coal and Gangue Recognition Algorithm
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作者：Kuidong Gao ; Changlong Du ; Haoxiang Wang 等
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2013
卷号：6
期号：4
出版社：SERSC
摘要：The separation for coal and gangue is an important process in mine production. In order to automatically select the gangue, this study obtained the difference and distribution regularity of the grayscale distribution by analyzing a large number of image data. By improving Bayesian Decision theory, identifiably character Bayesian Discriminant algorithm was proposed to get grayscale division threshold of coal and gangue. Aimed at the problem that impurities gangue and vitrinite in coal affect the accuracy of recognition result, mean smoothing filter algorithm was used to pre-process image and related neighborhood pixels recognition algorithm was proposed for recognizing the coal and gangue. The recognition system was tested on-line with a large number of random selected materials for many times, the average correct recognition was 96.8%. The test results indicated that the algorithm is stable and robust and the recognition system has a great potential in automatic selecting of gangue.
关键词：coal and gangue; grayscale intensity; bayesian decision theory; threshold; image processing