A machine vision approach to the grading of crushed aggregate

Fionn Murtagh, Xiaoyu Qiao, Danny Crookes, Paul Walsh, P. A Muhammed Basheer, Adrian Long, Jean Luc Starck

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
Issue number4
Early online date10 Jun 2005
DOIs
Publication statusPublished - 1 Sep 2005
Externally publishedYes

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