A comparison of dimension reduction methods with application to multi-spectral images of sand used in concrete

This paper presents a comparison of dimension reduction methods based on a novel machine vision application for estimating moisture content in sand used to make concrete. For the application in question it is very important to know the moisture content of the sand so as to ensure good-quality concre...

Full description

Saved in:
Bibliographic Details
Published in:Machine vision and applications 2010-10, Vol.21 (6), p.959-968
Main Authors: Clemmensen, Line H., Hansen, Michael E., Ersbøll, Bjarne K.
Format: Article
Language:English
Subjects:
Algorithms
Communications Engineering
Computer Science
Concretes
Image Processing and Computer Vision
Mathematical models
Moisture content
Networks
Pattern Recognition
Reduction
Sand
Short Paper
Summaries
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a comparison of dimension reduction methods based on a novel machine vision application for estimating moisture content in sand used to make concrete. For the application in question it is very important to know the moisture content of the sand so as to ensure good-quality concrete. In order to achieve a continuous in-line approach for the concrete mixing, digital image analysis is used. Multi-spectral images, consisting of nine spectral bands in the visible and near infrared (NIR) range, were acquired. Each image consists of approximately 9 million pixels. Five different sand types were examined with 20–60 images for each type. To reduce the amount of data, features were extracted from the multi-spectral images; the features were summary statistics on single bands and pairs of bands as well as morphological summaries. The number of features (2,016) is high in relation to the number of observations and, therefore, dimension reductive methods are needed. Furthermore, speed, which is an important consideration, is aided by the use of a small number of variables. On top of that, fewer dimensions tend to give more robust results. Two traditional statistical methods for dimension reduction (forward selection and principal components) combined with ordinary least squares and one sophisticated chemometrics algorithm (genetic algorithm-partial least squares) are compared to the recently proposed least angle regression-elastic net (LARS-EN) model selection method.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-009-0193-z