A comparative analysis of different approaches to target differentiation and localization using infrared sensors

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buir.advisorBarshan, Billur
dc.contributor.authorAytaƧ, Tayfun
dc.date.accessioned2016-07-01T11:09:27Z
dc.date.available2016-07-01T11:09:27Z
dc.date.issued2006
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractThis study compares the performances of various techniques for the differentiation and localization of commonly encountered features in indoor environments, such as planes, corners, edges, and cylinders, possibly with different surface properties, using simple infrared sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and differentiation process. The techniques considered include rule-based, template-based, and neural network-based target differentiation, parametric surface differentiation, and statistical pattern recognition techniques such as parametric density estimation, various linear and quadratic classifiers, mixture of normals, kernel estimator, k-nearest neighbor, artificial neural network, and support vector machine classi- fiers. The geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. For a set of six surfaces, we get a correct differentiation rate of 100% in parametric differentiation based on reflection modeling. The results demonstrate that simple infrared sensors, when coupled with appropriate processing, can be used to extract substantially more information than such devices are commonly employed for. The demonstrated system would find application in intelligent autonomous systems such as mobile robots whose task involves surveying an unknown environment made of different geometry and surface types. Industrial applications where different materials/surfaces must be identified and separated may also benefit from this approach.en_US
dc.description.degreePh.D.en_US
dc.description.statementofresponsibilityAytaƧ, Tayfunen_US
dc.format.extentxix, 126 leaves, graphics+ 1 CD-ROMen_US
dc.identifier.itemidBILKUTUPB102023
dc.identifier.urihttp://hdl.handle.net/11693/29952
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInfrared sensorsen_US
dc.subjectOptical sensingen_US
dc.subjectTarget differentiationen_US
dc.subjectTarget localizationen_US
dc.subjectSurface recognitionen_US
dc.subjectPosition estimationen_US
dc.subjectFeature extractionen_US
dc.subjectStatistical pattern recognitionen_US
dc.subjectArtificial neural networksen_US
dc.subject.lccTA1570 .A983 2006en_US
dc.subject.lcshInfrared technology.en_US
dc.titleA comparative analysis of different approaches to target differentiation and localization using infrared sensorsen_US
dc.typeThesisen_US
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