Estimation of Critical Parameters in Concrete Production Using Multispectral Vision Technology

AbstractWe analyze multispectral reflectance images of concrete aggregate material, and design computational measures of the important and critical parameters used in concrete production. The features extracted from the images are exploited as explanatory variables in regression models and used to predict aggregate type, water content, and size distribution. We analyze and validate the methods on five representative aggregate types, commonly used in concrete production. Using cross validation, the generated models proves to have a high performance in predicting all of the critical parameters.
Keywordsimage analysis; critical parameters; concrete; water content; size distribution; type; aggregate
TypeConference paper [With referee]
ConferenceLecture Notes in Computer Science, LNCS3540
Year2005    pp. 1228-1237
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
SeriesLecture Notes in Computer Science
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics