Efficient feature detection using OBAloG: optimized box approximation of Laplacian of Gaussian

dc.contributor.authorJakkula, Vinayak Reddy
dc.date.accessioned2010-04-19T16:39:21Z
dc.date.available2010-04-19T16:39:21Z
dc.date.graduationmonthMay
dc.date.issued2010-04-19T16:39:21Z
dc.date.published2010
dc.description.abstractThis thesis presents a novel approach for detecting robust and scale invariant interest points in images. The detector accurately and efficiently approximates the Laplacian of Gaussian using an optimal set of weighted box filters that take advantage of integral images to reduce computations. When combined with state-of-the art descriptors for matching, the algorithm performs better than leading feature tracking algorithms including SIFT and SURF in terms of speed and accuracy.
dc.description.advisorChristopher L. Lewis
dc.description.degreeMaster of Science
dc.description.departmentDepartment of Electrical and Computer Engineering
dc.description.levelMasters
dc.identifier.urihttp://hdl.handle.net/2097/3651
dc.language.isoen_US
dc.publisherKansas State University
dc.rights© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectFeature Extraction
dc.subjectLaplacian of Gaussian
dc.subject.umiEngineering, Electronics and Electrical (0544)
dc.titleEfficient feature detection using OBAloG: optimized box approximation of Laplacian of Gaussian
dc.typeThesis

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