Drivable region detection for autonomous robots applied to South African underground mining

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dc.contributor.advisorBagula, Antoineen_ZA
dc.contributor.authorFalola, Omowunmi Elizabethen_ZA
dc.date.accessioned2014-12-29T05:04:51Z
dc.date.available2014-12-29T05:04:51Z
dc.date.issued2012en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThis dissertation focuses on enhancing autonomous robots' capability to identify drivable regions in underground terrains. A system model that compares the drivability analysis of underground terrains using the entropy model and statistical region merging (SRM) was developed, with a view to presenting an analysis of 2D and 3D results. The approach involves standard image-processing techniques, such as colour and texture feature extraction and region segmentation for underground image classification. A probabilistic method based on the local entropy was employed. The entropy is measured within a fixed window on each frame in order to compute features used in the segmentation process. This research compares the results obtained from the entropy method and SRM approach. Performance evaluation is carried out to provide useful qualitative and quantitative conclusions.en_ZA
dc.identifier.apacitationFalola, O. E. (2012). <i>Drivable region detection for autonomous robots applied to South African underground mining</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/10492en_ZA
dc.identifier.chicagocitationFalola, Omowunmi Elizabeth. <i>"Drivable region detection for autonomous robots applied to South African underground mining."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2012. http://hdl.handle.net/11427/10492en_ZA
dc.identifier.citationFalola, O. 2012. Drivable region detection for autonomous robots applied to South African underground mining. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Falola, Omowunmi Elizabeth AB - This dissertation focuses on enhancing autonomous robots' capability to identify drivable regions in underground terrains. A system model that compares the drivability analysis of underground terrains using the entropy model and statistical region merging (SRM) was developed, with a view to presenting an analysis of 2D and 3D results. The approach involves standard image-processing techniques, such as colour and texture feature extraction and region segmentation for underground image classification. A probabilistic method based on the local entropy was employed. The entropy is measured within a fixed window on each frame in order to compute features used in the segmentation process. This research compares the results obtained from the entropy method and SRM approach. Performance evaluation is carried out to provide useful qualitative and quantitative conclusions. DA - 2012 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2012 T1 - Drivable region detection for autonomous robots applied to South African underground mining TI - Drivable region detection for autonomous robots applied to South African underground mining UR - http://hdl.handle.net/11427/10492 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/10492
dc.identifier.vancouvercitationFalola OE. Drivable region detection for autonomous robots applied to South African underground mining. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2012 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/10492en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherComputer Scienceen_ZA
dc.titleDrivable region detection for autonomous robots applied to South African underground miningen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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