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Robust segmentation and object classification in natural and medical images
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Robust segmentation and object classification in natural and medical images
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ETD_1536
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051426
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eng
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theses
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Electrical and Computer Engineering
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Computer vision
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Image processing
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Diagnostic imaging
Abstract
Image 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.
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electronic resource
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xiii, 109 p. : ill.
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Ph.D.
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Includes bibliographical references (p. 96-106)
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by Lin Yang
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Yang
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Lin
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1977
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Lin Yang
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Peter
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chair
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Peter Meer
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Dana
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Kristin
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Kristin Dana
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Zhang
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Yanyong
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Yanyong Zhang
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Parashar
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Manish
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Manish Parashar
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Foran
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David
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David J. Foran
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-05
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xx
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Rutgers University Electronic Theses and Dissertations
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Graduate School - New Brunswick Electronic Theses and Dissertations
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doi:10.7282/T3DN458J
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ETD doctoral
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Yang
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Lin
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Lin Yang
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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