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A machine vision approach to the grading of crushed aggregate

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Abstract

The grading of crushed aggregate is carried out usually by sieving. We describe a new image-based approach to the automatic grading of such materials. The operational problem addressed is where the camera is located directly over a conveyor belt. Our approach characterizes the information content of each image, taking into account relative variation in the pixel data, and resolution scale. In feature space, we find very good class separation using a multidimensional linear classifier. The innovation in this work includes (i) introducing an effective image-based approach into this application area, and (ii) our supervised classification using wavelet entropy-based features.

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Authors and Affiliations

  1. Department of Computer Science, University of London, Royal Holloway Egham, Surrey, TW20 0EX, UK

    Fionn Murtagh

  2. School of Computer Science, Queen's University Belfast, Belfast, BT7 1NN, UK

    Xiaoyu Qiao & Danny Crookes

  3. School of Civil Engineering, Queen's University Belfast, Belfast, BT7 1NN, UK

    Paul Walsh, P. A. Muhammed Basheer & Adrian Long

  4. DAPNIA/SEI-SAP, CEA-Saclay, 91191, Gif-sur-Yvette Cedex, France

    Jean-Luc Starck

Authors
  1. Fionn Murtagh
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  2. Xiaoyu Qiao
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  3. Danny Crookes
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  4. Paul Walsh
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  5. P. A. Muhammed Basheer
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  6. Adrian Long
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  7. Jean-Luc Starck
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Correspondence to Fionn Murtagh.

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Murtagh, F., Qiao, X., Crookes, D. et al. A machine vision approach to the grading of crushed aggregate. Machine Vision and Applications 16, 229–235 (2005). https://doi.org/10.1007/s00138-005-0176-7

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  • DOI: https://doi.org/10.1007/s00138-005-0176-7

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