Toward Automated Quality Classification via Statistical Modeling of Grain Images for Rice Processing Monitoring. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Liu, Jinping; Tang, Zhaohui; Chen, Qing; Xu, Pengfei; Liu, Wenzhong; Zhu, Jianyong
• Is Part Of: International journal of computational intelligence systems. Volume 9:Issue 1(2016)
• Page Start: 120
• Page End: 132
• Publication Date: 2016-01-02
• Subjects: Process monitoring -- Sequential fragmentation theory -- Weibull distribution -- Least squares-support vector machine (LS-SVM); Computational intelligence -- Periodicals; 006.305
• DOI: 10.1080/18756891.2016.1144158
• Languages: English
• ISSNs: 1875-6891

Abstract

Abstract: Computer vision-based rice quality inspection has recently attracted increasing interest in both academic and industrial communities because it is a low-cost tool for fast, non-contact, nondestructive, accurate and objective process monitoring. However, current computer-vision system is far from effective in intelligent perception of complex grainy images, comprised of a large number of local homogeneous particles or fragmentations without obvious foreground and background. We introduce a well known statistical modeling theory of size distribution in comminution processes, sequential fragmentation theory, for the visual analysis of the spatial structure of the complex grainy images. A kind of omnidirectional multi-scale Gaussian derivative filter-based image statistical modeling method is presented to attain omnidirectional structural features of grain images under different observation scales. A modified LS-SVM classifier is subsequently established to automatically identify the processing rice quality. Extensive confirmative and comparative tests indicate the effectiveness and outperformance of the proposed method.