| Summary: | 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. === Graduation date: 2004
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