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RUcore Resource Object
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TitleRobust segmentation and object classification in natural and medical images
NameYang, Lin (author), Meer, Peter (chair), Dana, Kristin (internal member), Zhang, Yanyong (internal member), Parashar, Manish (internal member), Foran, David (outside member), Rutgers University, Graduate School - New Brunswick,
Degree Date2009-05
Date Created2009
SubjectElectrical and Computer Engineering,
Computer vision,
Image processing,
Diagnostic imaging
DescriptionImage segmentation and object classification are two fundamental tasks in computer vision. In this thesis, a novel segmentation algorithm based on deformable model and robust estimation is introduced to produce reliable segmentation results. The algorithm is extended to handle touching object and partially occluded image segmentation. A multiple class segmentation algorithm is described to achieve multi-class "object cut". The accurate results are
achieved using the appearance and bag of keypoints models integrated over mean-shift patches. An affine invariant descriptor is proposed to model the spatial configuration of the keypoints. Besides working with 2D image segmentation problem, a robust, fast and accurate segmentation algorithm is illustrated for processing 4D volumetric data. One-step forward prediction is applied to generate the motion prior based on motion modes learning. Two collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Multi-class classification algorithms using a gentle boosting is used to classify three types of breast cancer. The algorithm is Grid-enabled and launched on the IBM World Community Grid. We will introduce a fast and robust image registration algorithm for both 2D and 3D images. The algorithm starts from an automatic detection of the landmarks followed by a coarse to fine estimation of the nonlinear mapping. The parallelization of the algorithm on the IBM Cell Broadband Engine (IBM Cell/B.E.) will also be explained in details.
achieved using the appearance and bag of keypoints models integrated over mean-shift patches. An affine invariant descriptor is proposed to model the spatial configuration of the keypoints. Besides working with 2D image segmentation problem, a robust, fast and accurate segmentation algorithm is illustrated for processing 4D volumetric data. One-step forward prediction is applied to generate the motion prior based on motion modes learning. Two collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Multi-class classification algorithms using a gentle boosting is used to classify three types of breast cancer. The algorithm is Grid-enabled and launched on the IBM World Community Grid. We will introduce a fast and robust image registration algorithm for both 2D and 3D images. The algorithm starts from an automatic detection of the landmarks followed by a coarse to fine estimation of the nonlinear mapping. The parallelization of the algorithm on the IBM Cell Broadband Engine (IBM Cell/B.E.) will also be explained in details.
NotePh.D.
NoteIncludes bibliographical references (p. 96-106)
Noteby Lin Yang
Genretheses
Persistent URLhttp://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051426
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.