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

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/sb397c439

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  • 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.
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  • 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.
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  • description.provenance : Made available in DSpace on 2012-06-21T18:36:49Z (GMT). No. of bitstreams: 1 JiaJin2004.pdf: 3769055 bytes, checksum: ba3d14b149b61619091a74eb9481fe40 (MD5) Previous issue date: 2004-04-12
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-06-21T18:36:49Z (GMT) No. of bitstreams: 1 JiaJin2004.pdf: 3769055 bytes, checksum: ba3d14b149b61619091a74eb9481fe40 (MD5)
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