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.
| Language | English |
|---|---|
| Pages | 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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A machine vision approach to the grading of crushed aggregate. / Murtagh, Fionn; Qiao, Xiaoyu; Crookes, Danny; Walsh, Paul; Basheer, P. A Muhammed; Long, Adrian; Starck, Jean Luc.
In: Machine Vision and Applications, Vol. 16, No. 4, 01.09.2005, p. 229-235.Research output: Contribution to journal › Article
TY - JOUR
T1 - A machine vision approach to the grading of crushed aggregate
AU - Murtagh, Fionn
AU - Qiao, Xiaoyu
AU - Crookes, Danny
AU - Walsh, Paul
AU - Basheer, P. A Muhammed
AU - Long, Adrian
AU - Starck, Jean Luc
PY - 2005/9/1
Y1 - 2005/9/1
N2 - 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.
AB - 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.
KW - Aggregate
KW - Construction
KW - Entropy
KW - Image database
KW - Information
KW - Machine vision
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=27744554854&partnerID=8YFLogxK
U2 - 10.1007/s00138-005-0176-7
DO - 10.1007/s00138-005-0176-7
M3 - Article
VL - 16
SP - 229
EP - 235
JO - Machine Vision and Applications
T2 - Machine Vision and Applications
JF - Machine Vision and Applications
SN - 0932-8092
IS - 4
ER -