TY - JOUR
T1 - Grading of construction aggregate through machine vision
T2 - Results and prospects
AU - Murtagh, Fionn
AU - Qiao, Xiaoyu
AU - Walsh, Paul
AU - Basheer, P. A.M.
AU - Crookes, Danny
AU - Long, Adrian
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Traditionally, crushed aggregate to be used in construction is graded using sieves. We describe an innovative machine vision approach to such grading. Our operational scenario is one where a camera takes images from directly overhead of a layer of aggregate on a conveyor belt. In this article, we describe effective solutions for (i) image segmentation, allowing larger pieces of aggregate to be measured and (ii) supervised classification from wavelet entropy features, for class assignment of both finer and coarse aggregate.
AB - Traditionally, crushed aggregate to be used in construction is graded using sieves. We describe an innovative machine vision approach to such grading. Our operational scenario is one where a camera takes images from directly overhead of a layer of aggregate on a conveyor belt. In this article, we describe effective solutions for (i) image segmentation, allowing larger pieces of aggregate to be measured and (ii) supervised classification from wavelet entropy features, for class assignment of both finer and coarse aggregate.
KW - Construction industry
KW - Image database
KW - Machine vision
KW - Supervised and unsupervised classification
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=27744602983&partnerID=8YFLogxK
U2 - 10.1016/j.compind.2005.05.016
DO - 10.1016/j.compind.2005.05.016
M3 - Article
AN - SCOPUS:27744602983
VL - 56
SP - 905
EP - 917
JO - Computers in Industry
JF - Computers in Industry
SN - 0166-3615
IS - 8-9
ER -