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Object highlighting : real-time boundary detection using a Bayesian network

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dc.contributor.advisor Mortensen, Eric
dc.creator Jia, Jin
dc.date.accessioned 2012-06-21T18:36:49Z
dc.date.available 2012-06-21T18:36:49Z
dc.date.copyright 2004-04-12
dc.date.issued 2004-04-12
dc.identifier.uri http://hdl.handle.net/1957/30045
dc.description Graduation date: 2004 en_US
dc.description.abstract Image segmentation continues to be a fundamental problem in computer vision and image understanding. In this thesis, we present a Bayesian network that we use for object boundary detection in which the MPE (most probable explanation) before any evidence can produce multiple non-overlapping, non-self-intersecting closed contours and the MPE with evidence where one or more connected boundary points are provided produces a single non-self-intersecting, closed contour that accurately defines an object's boundary. We also present a near-linear-time algorithm that determines the MPE by computing the minimum-path spanning tree of a weighted, planar graph and finding the excluded edge (i.e., an edge not in the spanning tree) that forms the most probable loop. This efficient algorithm allows for real-time feedback in an interactive environment in which every mouse movement produces a recomputation of the MPE based on the new evidence (i.e., the new cursor position) and displays the corresponding closed loop. We call this interface "object highlighting" since the boundary of various objects and sub-objects appear and disappear as the mouse cursor moves around within an image. en_US
dc.language.iso en_US en_US
dc.subject.lcsh Image processing -- Digital techniques en_US
dc.subject.lcsh Image analysis en_US
dc.subject.lcsh Computer vision en_US
dc.title Object highlighting : real-time boundary detection using a Bayesian network en_US
dc.type Thesis/Dissertation en_US
dc.degree.name Master of Science (M.S.) in Computer Science en_US
dc.degree.level Master's en_US
dc.degree.discipline Engineering en_US
dc.degree.grantor Oregon State University en_US
dc.contributor.committeemember Metoyer, Ron
dc.contributor.committeemember Tadepalli, Prasad
dc.contributor.committeemember Birkes, David
dc.description.digitization File scanned at 300 ppi (Monochrome, 256 Grayscale, 24-bit Color) using Capture Perfect 3.0 on a Canon DR-9050C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR. en_US
dc.description.peerreview no en_us


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