Grading of construction aggregate through machine vision: Results and prospects

Fionn Murtagh; Xiaoyu Qiao; Paul Walsh; P. A.M. Basheer; Danny Crookes; Adrian Long

doi:10.1016/j.compind.2005.05.016

Grading of construction aggregate through machine vision: Results and prospects

Fionn Murtagh, Xiaoyu Qiao, Paul Walsh, P. A.M. Basheer, Danny Crookes, Adrian Long

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	905-917
Number of pages	13
Journal	Computers in Industry
Volume	56
Issue number	8-9
Early online date	10 Oct 2005
DOIs	https://doi.org/10.1016/j.compind.2005.05.016
Publication status	Published - 1 Dec 2005
Externally published	Yes

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Cite this

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title = "Grading of construction aggregate through machine vision: Results and prospects",

abstract = "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.",

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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

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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.

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KW - Construction industry

KW - Image database

KW - Machine vision

KW - Supervised and unsupervised classification

KW - Wavelet transform

UR - https://www.scopus.com/pages/publications/27744602983

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JO - Computers in Industry

JF - Computers in Industry

IS - 8-9

ER -

Grading of construction aggregate through machine vision: Results and prospects

Abstract

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