Imaging system for detection, classification and quantification

Examensarbete för masterexamen

Please use this identifier to cite or link to this item:
Download file(s):
File Description SizeFormat 
203599.pdfFulltext6.45 MBAdobe PDFThumbnail
Bibliographical item details
Type: Examensarbete för masterexamen
Master Thesis
Title: Imaging system for detection, classification and quantification
Authors: Pfreundschuh, Simon
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++.
Keywords: Grundläggande vetenskaper;Matematik;Basic Sciences;Mathematics
Issue Date: 2014
Publisher: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper
Chalmers University of Technology / Department of Mathematical Sciences
Collection:Examensarbeten för masterexamen // Master Theses

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.