Abstract
The measurement of stacked-sheet quantity is an essential step in packaging and printing production, and its counting accuracy has a direct impact on economic efficiency of related companies. With its noncontact, non-destructivity and real-time measurement merits, the machine vision method has been widely applied to quality control for high-end printing products. In this paper, we aim to circumvent the fringe detection problem in stacked-sheet images by introducing a level line guided line-segment growing algorithm. Then, a high-accuracy measurement of stack quantity can be realized with the improvement of precision and completeness on fringe identification.Our work mainly consists of three parts: 1) A unidirectional gradient operator is adopted to eliminate multiple responses on a single fringe. 2) The gradient magnitude and level-line direction are combined to improve the growth of line support regions in noisy environment. 3) To completely identify each sheet fringe, a connected component analysis algorithm is integrated to remedy the local gap in line detection. The performance of our algorithm has been verified in experiments using various kinds of printed-papers with a large number. It is shown that the long-term measurement error is less than 0.75‰ and is sufficient to meet the requirement of factory applications.
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Gang, Z., Shuo, Y., Xiao, C. (2014). A Fast Straight-Line Growing Algorithm for Sheet-Counting with Stacked-Paper Images. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_43
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DOI: https://doi.org/10.1007/978-3-662-45646-0_43
Publisher Name: Springer, Berlin, Heidelberg
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