TY - THES
AU - Yurick, Micheal
PY - 2006
TI - A region-based filter for video segmentation
KW - Thesis/Dissertation
LA - eng
M3 - Text
AB - This thesis addresses the problem of extracting object masks from video sequences.
It presents an online, dynamic system for creating appearance masks
of an arbitrary object of interest contained in a video sequence, while making
minimal assumptions about the appearance and motion of the objects and scene
being imaged. It examines a region-based approach, in contrast to more recently
popular pixel-wise approaches to segmentation to illustrate the advantages in
the reduction of the complexity of the labeling problem.
The redundancy of information typically present in a pixel-wise approach
is exploited by an initial oversegmentation of the current video frame. The
oversegmentation procedure is based upon a modified version of the classic
watershed segmentation algorithm. This oversegmentation produces a set of
appearance/motion-consistent regions upon which a conditional random field is
constructed. Observations at each region are collected based upon the colour
statistics within a region and the motion statistics as determined by the optical
flow over the region. An unparameterized model for both the object of interest
and the remainder of the scene are constructed on a frame by frame basis.
The conditional random field model is used in conjunction with a first order
hidden markov model over the frames of the sequence. Mean field approximations
for variational inference in this model produce a region-based filter framework
which incorporates both spatial and temporal constraints. This framework
is used to determine an appropriate labeling for each region in each frame. The
reduction in the complexity of the field model produced by the regions (as opposed
to pixels) results directly in a reduced cost for the labeling problem with
minor effects on accuracy.
N2 - This thesis addresses the problem of extracting object masks from video sequences.
It presents an online, dynamic system for creating appearance masks
of an arbitrary object of interest contained in a video sequence, while making
minimal assumptions about the appearance and motion of the objects and scene
being imaged. It examines a region-based approach, in contrast to more recently
popular pixel-wise approaches to segmentation to illustrate the advantages in
the reduction of the complexity of the labeling problem.
The redundancy of information typically present in a pixel-wise approach
is exploited by an initial oversegmentation of the current video frame. The
oversegmentation procedure is based upon a modified version of the classic
watershed segmentation algorithm. This oversegmentation produces a set of
appearance/motion-consistent regions upon which a conditional random field is
constructed. Observations at each region are collected based upon the colour
statistics within a region and the motion statistics as determined by the optical
flow over the region. An unparameterized model for both the object of interest
and the remainder of the scene are constructed on a frame by frame basis.
The conditional random field model is used in conjunction with a first order
hidden markov model over the frames of the sequence. Mean field approximations
for variational inference in this model produce a region-based filter framework
which incorporates both spatial and temporal constraints. This framework
is used to determine an appropriate labeling for each region in each frame. The
reduction in the complexity of the field model produced by the regions (as opposed
to pixels) results directly in a reduced cost for the labeling problem with
minor effects on accuracy.
UR - https://open.library.ubc.ca/collections/831/items/1.0052051
ER - End of Reference