Estimation and Classification through Regression with Variable Selection amongst Features Extracted from Multi-Spectral Images - Estimation of moisture content in sand & Identification of Penicillium fungi

Line Harder Clemmensen

AbstractThis report deals with identification of three different species of Penicillium fungi and estimation of moisture content in sand used to make concrete. Multi-spectral images of 9 or 18 bands are used to analyze samples of sand and fungi, respectively. The project covers the image acquisition of the samples, the identification of Regions Of Interest (ROIs) in the images, the feature extraction from the ROIs, and classification or es-timation based on the extracted features. The number of features extracted is much larger than the number of observations and the dimensionality is therefore a big issue in the analysis of the data. Traditional multivariate, statistical methods for variable selection, decomposition, classification, and regression are compared to newer methods that select variables and/or perform coefficient shrinkage within the regression. Dummy variables are constructed to use the newer methods for classification.
TypeMaster's thesis [Industrial collaboration]
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics