Computer vision system for identifying road signs using triangulation and bundle adjustment

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dc.contributor.author Krishnan, Anupama
dc.date.accessioned 2009-12-07T20:30:06Z
dc.date.available 2009-12-07T20:30:06Z
dc.date.issued 2009-12-07T20:30:06Z
dc.identifier.uri http://hdl.handle.net/2097/2244
dc.description.abstract This thesis describes the development of an automated computer vision system that identifies and inventories road signs from imagery acquired from the Kansas Department of Transportation's road profiling system that takes images every 26.4 feet on highways through out the state. Statistical models characterizing the typical size, color, and physical location of signs are used to help identify signs from the imagery. First, two phases of a computationally efficient K-Means clustering algorithm are applied to the images to achieve over-segmentation. The novel second phase ensures over-segmentation without excessive computation. Extremely large and very small segments are rejected. The remaining segments are then classified based on color. Finally, the frame to frame trajectories of sign colored segments are analyzed using triangulation and Bundle adjustment to determine their physical location relative to the road video log system. Objects having the appropriate color, and physical placement are entered into a sign database. To develop the statistical models used for classification, a representative set of images was segmented and manually labeled determining the joint probabilistic models characterizing the color and location typical to that of road signs. Receiver Operating Characteristic curves were generated and analyzed to adjust the thresholds for the class identification. This system was tested and its performance characteristics are presented. en_US
dc.description.sponsorship Kansas Department Of Transportation en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Classification en_US
dc.subject Triangulation en_US
dc.subject Bundle Adjustment en_US
dc.subject K-Means en_US
dc.subject Segmentation en_US
dc.subject mahalanobis distance en_US
dc.title Computer vision system for identifying road signs using triangulation and bundle adjustment en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Electrical and Computer Engineering en_US
dc.description.advisor Christopher L. Lewis en_US
dc.subject.umi Engineering, Electronics and Electrical (0544) en_US
dc.subject.umi Transportation (0709) en_US
dc.date.published 2009 en_US
dc.date.graduationmonth December en_US


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