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 language | English |
|---|---|
| Pages (from-to) | 229-235 |
| Number of pages | 7 |
| Journal | Machine Vision and Applications |
| Volume | 16 |
| Issue number | 4 |
| Early online date | 10 Jun 2005 |
| DOIs | |
| Publication status | Published - 1 Sep 2005 |
| Externally published | Yes |
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Cite this
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