Wang Hongyan
Experimental Teaching Center for Economics and Management, Nanjing University of Finance and Economics, 210046, Nanjing, China
Zhu Quanyin
Faculty of Computer Engineering, Huaiyin Institute of Technology, 223005, Huaian, China
ABSTRACT
In order to improve the speed of brands recognition of cigarette carton automatic sorting for tobacco logistics tobacconist, the effect factor such as speed of camera frame capturing, the communication speed between camera with computer and imaging quality were analyzed. Furthermore, In order to increase the efficiency of the complexity indexing algorithm, five operators of color recognition, complexity recognition, brands recognition, color of brands recognition, length measure recognition and contour extraction algorithm were optimized. The proposed method not only decreased the calculate times of multi-recognition algorithm, but also optimized the data structure of the recognition automatic sorting system. The experiment results demonstrated that the average time of proposed system was very low; it was obtained less than 50 millisecond average per-frame. The proposed method could satisfy the tobacconist application requirements on the automatic sorting of tobacco recognition and proved that this model is meaningful and useful to the same trade. This method provides references for the LVQ neural network.
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How to cite this article
Wang Hongyan and Zhu Quanyin, 2013. Algorithm of Time Optimization on Brands Recognition for Automatic Sorting System. Information Technology Journal, 12: 3398-3404.
DOI: 10.3923/itj.2013.3398.3404
URL: https://scialert.net/abstract/?doi=itj.2013.3398.3404
DOI: 10.3923/itj.2013.3398.3404
URL: https://scialert.net/abstract/?doi=itj.2013.3398.3404
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