Multispectral Image Analysis for Object Recognition and Classification

FieldValue
dc.contributor.authorViau, Claude
dc.date.accessioned2016-04-21T14:22:26Z
dc.date.available2016-04-21T14:22:26Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10393/34532
dc.identifier.urihttp://dx.doi.org/10.20381/ruor-5620
dc.description.abstractComputer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate some form of decision-making process. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various field including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objectives of this research project were to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. The goal was not to find a new way to “fuse” the visual and thermal images together but rather establish a methodology to extract multispectral descriptors in order to improve a machine vision system’s ability to recognize specific classes of objects.A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM’s class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets. Commonly used performance metrics were applied to assess the sensitivity, specificity and accuracy of each classifier. The research demonstrated that the highest recognition rate was achieved by an expert system (multiple classifiers) that combined the expertise of the visual-only classifier, the thermal-only classifier and the combined visual-thermal classifier.
dc.language.isoen
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectmultispectral
dc.subjectobject recognition
dc.subjectvisual thermal imagery
dc.subjectsupport vector machines (SVM)
dc.subjectclassification
dc.titleMultispectral Image Analysis for Object Recognition and Classification
dc.typeThesis
dc.contributor.supervisorPayeur, Pierre
dc.contributor.supervisorCretu, Ana-Maria
thesis.degree.nameMASc
thesis.degree.levelMasters
thesis.degree.disciplineGénie / Engineering
uottawa.departmentSchool of Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -

Files