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Harvard
Pfreundschuh, S. (2014) Imaging system for detection, classification and quantification. Göteborg : Chalmers University of Technology
BibTeX
@mastersthesis{
Pfreundschuh2014,
author={Pfreundschuh, Simon},
title={Imaging system for detection, classification and quantification},
abstract={This thesis was conducted at the Fraunhofer Chalmers Centre for Industrial Mathematics
in collaboration with the Fraunhofer-Institut f¨ur Techno- und Wirtschaftsmathematik.
The aim of this thesis is to develop an imaging system for the automated detection of
holes in images of supermarket shelves. The proposed approach uses an unsupervised
segmentation method to presegment the image into homogeneous regions. Each of those
image regions is then classified separately using a support vector machine. Finally,
suitable bounding boxes are found for image regions that are likely to represent holes.
Apart from the SVM classifier also an AdaBoost classifier and a structural classifier
based on conditional random fields are implemented and tested. This thesis describes the
implementation and performance characteristics of the resulting imaging system, which
is implemented using the ToolIP graphical image processing framework and C++.},
publisher={Institutionen för matematiska vetenskaper, Chalmers tekniska högskola},
place={Göteborg},
year={2014},
}
RefWorks
RT Generic
SR Electronic
ID 203599
A1 Pfreundschuh, Simon
T1 Imaging system for detection, classification and quantification
YR 2014
AB This thesis was conducted at the Fraunhofer Chalmers Centre for Industrial Mathematics
in collaboration with the Fraunhofer-Institut f¨ur Techno- und Wirtschaftsmathematik.
The aim of this thesis is to develop an imaging system for the automated detection of
holes in images of supermarket shelves. The proposed approach uses an unsupervised
segmentation method to presegment the image into homogeneous regions. Each of those
image regions is then classified separately using a support vector machine. Finally,
suitable bounding boxes are found for image regions that are likely to represent holes.
Apart from the SVM classifier also an AdaBoost classifier and a structural classifier
based on conditional random fields are implemented and tested. This thesis describes the
implementation and performance characteristics of the resulting imaging system, which
is implemented using the ToolIP graphical image processing framework and C++.
PB Institutionen för matematiska vetenskaper, Chalmers tekniska högskola,
LA eng
LK http://publications.lib.chalmers.se/records/fulltext/203599/203599.pdf
OL 30
