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Author
Title Non-local active contours
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Publication Date
Date Accessioned
Degree PhD
Discipline/Department Electrical and Computer Engineering
Degree Level doctoral
University/Publisher Georgia Tech
Abstract This thesis deals with image segmentation problems that arise in various computer vision related fields such as medical imaging, satellite imaging, video surveillance, recognition and robotic vision. More specifically, this thesis deals with a special class of image segmentation technique called Snakes or Active Contour Models. In active contour models, image segmentation is posed as an energy minimization problem, where an objective energy function (based on certain image related features) is defined on the segmenting curve (contour). Typically, a gradient descent energy minimization approach is used to drive the initial contour towards a minimum for the defined energy. The drawback associated with this approach is that the contour has a tendency to get stuck at undesired local minima caused by subtle and undesired image features/edges. Thus, active contour based curve evolution approaches are very sensitive to initialization and noise. The central theme of this thesis is to develop techniques that can make active contour models robust against certain classes of local minima by incorporating global information in energy minimization. These techniques lead to energy minimization with global considerations; we call these models  – 'Non-local active contours'. In this thesis, we consider three widely used active contour models: 1) Edge- and region-based segmentation model, 2) Prior shape knowledge based segmentation model, and 3) Motion segmentation model. We analyze the traditional techniques used for these models and establish the need for robust models that avoid local minima. We address the local minima problem for each model by adding global image considerations.
Subjects/Keywords Active geodesics; Fast marching; Active contour models; Imge segmentation; Motion segmentation; Optical flow; Shape priors; Localized principal component analysis; Pattern perception; Pattern recognition systems; Computer vision
Contributors Dr. Yezzi, Anthony (Committee Chair); Dr. Barnes, Chris (Committee Member); Dr. Narasimha, Rajesh (Committee Member); Dr. Oshinski, John (Committee Member); Dr. Tannenbaum, Allen (Committee Member); Dr. Vela, Patricio (Committee Member)
Country of Publication us
Record ID handle:1853/44739
Repository gatech
Date Indexed 2020-05-13
Issued Date 2012-05-17 00:00:00
Note [degree] PhD; [advisor] Committee Chair: Dr. Yezzi, Anthony; Committee Member: Dr. Barnes, Chris; Committee Member: Dr. Narasimha, Rajesh; Committee Member: Dr. Oshinski, John; Committee Member: Dr. Tannenbaum, Allen; Committee Member: Dr. Vela, Patricio;

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