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Fast Measuring Particle Size by Using the Information of Particle Boundary and Shape

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Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

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Abstract

To quickly and accurately estimate average size of densely packed particles on a fast moving conveyor belt, a new image processing method is designed and studied. The method consists of two major algorithms, one is a one-pass boundary detection algorithm that is specially designed for the images of densely packed particles (the word “particle” is used in a wide sense), and the other is average size estimation based on image edge density. The algorithms are cooperative. The method has been tested experimentally for different kinds of closely packed particle images which are difficult to detect by ordinary image segmentation algorithms. The new method avoids delineating and measuring every particle on an image, therefore, is suitable for real-time imaging. It is particularly applicable for a densely packed and complicated particle image sequence.

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Authors and Affiliations

  1. Department of Computer Science & Technology, Chongqing University of Posts & Telecommunications, Post code: 400065, China

    Weixing Wang

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  1. Weixing Wang
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Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, W. (2006). Fast Measuring Particle Size by Using the Information of Particle Boundary and Shape. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_114

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  • DOI: https://doi.org/10.1007/11739685_114

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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