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Abstract Details
Activity Number:
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409
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Government Statistics
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Abstract - #306466 |
Title:
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Utilizing Changepoint Detection to Improve Boundary Tracking in Noisy Images
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Author(s):
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Alexander Chen*+
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Companies:
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SAMSI
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Address:
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19 TW Alexander, Res. Triangle Park, NC, 27709, United States
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Keywords:
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Change-point detection ;
CUSUM ;
Boundary tracking ;
Image processing ;
High dimensional data
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Abstract:
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This talk introduces improved algorithms for tracking boundaries in an image using change-point detection techniques. Boundary trackers move at the interface between two regions, weaving between them and making course corrections based on a decision function. By considering only local information, boundary tracking algorithms are able to follow boundaries very efficiently, making them quite suitable for work in large or high-dimensional images. As local algorithms, however, boundary tracking methods are extremely susceptible to noisy data or texture. We adapt the CUSUM algorithm to detect region changes on a local level, vastly improving tracking ability. Further improvements can be made by introducing a second change-point detection statistic to detect global off-boundary movement. We discuss the uncertainty involved in the tracking of noisy images and the extent to which false alarms and detection delays in change-point algorithms affect tracking. The adaptation of boundary tracking to the problem of image segmentation via hybrid schemes and applications to hyperspectral data and the tracking of fractal-like structures will also be discussed.
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Authors who are presenting talks have a * after their name.
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