Home
Resource
Robust segmentation and object classification in natural and medical images
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(9.06 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Yang, Lin. Robust segmentation and object classification in natural and medical images. Retrieved from https://doi.org/doi:10.7282/T3DN458J

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

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
Date Created
Other Date2009-05 (degree)
SubjectElectrical and Computer Engineering, Computer vision, Image processing, Diagnostic imaging
Extentxiii, 109 p. : ill.
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.
NotePh.D.
NoteIncludes bibliographical references (p. 96-106)
Noteby Lin Yang
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3DN458J
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.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024