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dc.contributor.advisorSaranlı, Uluç
dc.contributor.authorYıldız, Tuğba
dc.date.accessioned2016-01-08T18:14:27Z
dc.date.available2016-01-08T18:14:27Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/11693/15164
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2010.en_US
dc.descriptionIncludes bibliographical references leaves 113-129.en_US
dc.description.abstractOne of the basic problems to be addressed for a robot navigating in an outdoor environment is the tracking of its position and state. A fundamental first step in using algorithms for solving this problem, such as various visual Simultaneous Localization and Mapping (SLAM) strategies, is the extraction and identification of suitable stationary “landmarks” in the environment. This is particularly challenging in the outdoors geometrically consistent features such as lines are not frequent. In this thesis, we focus on using trees as persistent visual landmark features in outdoor settings. Existing work to this end only uses intensity information in images and does not work well in low-contrast settings. In contrast, we propose a novel method to incorporate both color and intensity information as well as regional attributes in an image towards robust of detection of tree trunks. We describe both extensions to the well-known edge-flow method as well as complementary Gabor-based edge detection methods to extract dominant edges in the vertical direction. The final stages of our algorithm then group these vertical edges into potential tree trunks using the integration of perceptual organization and all available image features. We characterize the detection performance of our algorithm for two different datasets, one homogeneous dataset with different images of the same tree types and a heterogeneous dataset with images taken from a much more diverse set of trees under more dramatic variations in illumination, viewpoint and background conditions. Our experiments show that our algorithm correctly finds up to 90% of trees with a false-positive rate lower than 15% in both datasets. These results establish that the integration of all available color, intensity and structure information results in a high performance tree trunk detection system that is suitable for use within a SLAM framework that outperforms other methods that only use image intensity information.en_US
dc.description.statementofresponsibilityYıldız, Tuğbaen_US
dc.format.extentxx, 141 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEdge detection, perceptual grouping, color, Gabor wavelets,en_US
dc.subjectObject detectionen_US
dc.subjectTree trunk detectionen_US
dc.subjectVisual landmarksen_US
dc.subjectVisual SLAMen_US
dc.subjectComputer visionen_US
dc.subjectPattern recognitionen_US
dc.subjectImage processingen_US
dc.subjectPerceptual groupingen_US
dc.subjectGabor waveletsen_US
dc.subject.lccTA1637 .Y553 2010en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshComputer simulation.en_US
dc.subject.lcshComputer vision.en_US
dc.subject.lcshSLAM (Computer program language)en_US
dc.subject.lcshRobots--Control systems.en_US
dc.subject.lcshWavelets (Mathematics)en_US
dc.subject.lcshPattern recognition systems.en_US
dc.titleDetection of tree trunks as visual landmarks in outdoor environmentsen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB122779


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