A machine vision approach to the grading of crushed aggregate

Daniel Crookes, Fionn Murtagh, Xiaoyu Qiao, Muhammed Basheer, Adrian Long, J.L. Starck, Paul Walsh

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)229-235
Number of pages7
JournalMachine Vision and Applications
Volume16(4)
Issue number4
DOIs
Publication statusPublished - Sep 2005

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition

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  • Cite this

    Crookes, D., Murtagh, F., Qiao, X., Basheer, M., Long, A., Starck, J. L., & Walsh, P. (2005). A machine vision approach to the grading of crushed aggregate. Machine Vision and Applications, 16(4)(4), 229-235. https://doi.org/10.1007/s00138-005-0176-7