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Postgraduate Thesis: Image point matching in multiple-view object reconstruction from imagesequences
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TitleImage point matching in multiple-view object reconstruction from imagesequences
 
AuthorsZhang, Jian
张简
 
Issue Date2012
 
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
 
AbstractThis thesis is concerned with three-dimensional (3D) reconstruction and point registration, which are fundamental topics of numerous applications in the area of computer vision. First, we propose the multiple epipolar lines (MEL) shape recovery method for 3D reconstruction from an image sequence captured under circular motion. This method involves recovering the 3D shape by reconstructing a set of 3D rim curves. The position of each point on a 3D rim curve is estimated by using three or more views. Two or more of these views are chosen close to each other to guarantee good image point matching, while one or more views are chosen far from these views to properly compensate for the error introduced in the triangulation scheme by the short baseline of the close views. Image point matching among all views is performed using a new method that suitably combines epipolar geometry and cross-correlation. Second, we develop the one line search (OLS) method for estimating the 3D model of an object from a sequence of images. The recovered object comprises a set of 3D rim curves. The OLS method determines the image point correspondences of each 3D point through a single line search along the ray defined by the camera center and each two-dimensional (2D) point where a photo-consistency index is maximized. In accordance with the approach, the search area is independently reduced to a line segment on the number of views. The key advantage of the proposed method is that only one variable is focused on in defining the corresponding 3D point, whereas the approaches for multiple-view stereo typically exploit multiple epipolar lines and hence require multiple variables. Third, we propose the expectation conditional maximization for point registration (ECMPR) algorithm to solve the rigid point registration problem by fitting the problem into the framework of maximum likelihood with missing data. The unknown correspondences are handled via mixture models. We derive a maximization criterion based on the expected complete-data log-likelihood. Then, the point registration problem can be solved by an instance of the expectation conditional maximization algorithm, that is, the ECMPR algorithm. Experiments with synthetic and real data are presented in each section. The proposed approaches provide satisfactory and promising results.
 
AdvisorsChesi, G
Hung, YS
 
DegreeDoctor of Philosophy
 
SubjectThree-dimensional imaging.
Image reconstruction.
Image processing.
 
Dept/ProgramElectrical and Electronic Engineering
 
DC FieldValue
dc.contributor.advisorChesi, G
 
dc.contributor.advisorHung, YS
 
dc.contributor.authorZhang, Jian
 
dc.contributor.author张简
 
dc.date.hkucongregation2012
 
dc.date.issued2012
 
dc.description.abstractThis thesis is concerned with three-dimensional (3D) reconstruction and point registration, which are fundamental topics of numerous applications in the area of computer vision. First, we propose the multiple epipolar lines (MEL) shape recovery method for 3D reconstruction from an image sequence captured under circular motion. This method involves recovering the 3D shape by reconstructing a set of 3D rim curves. The position of each point on a 3D rim curve is estimated by using three or more views. Two or more of these views are chosen close to each other to guarantee good image point matching, while one or more views are chosen far from these views to properly compensate for the error introduced in the triangulation scheme by the short baseline of the close views. Image point matching among all views is performed using a new method that suitably combines epipolar geometry and cross-correlation. Second, we develop the one line search (OLS) method for estimating the 3D model of an object from a sequence of images. The recovered object comprises a set of 3D rim curves. The OLS method determines the image point correspondences of each 3D point through a single line search along the ray defined by the camera center and each two-dimensional (2D) point where a photo-consistency index is maximized. In accordance with the approach, the search area is independently reduced to a line segment on the number of views. The key advantage of the proposed method is that only one variable is focused on in defining the corresponding 3D point, whereas the approaches for multiple-view stereo typically exploit multiple epipolar lines and hence require multiple variables. Third, we propose the expectation conditional maximization for point registration (ECMPR) algorithm to solve the rigid point registration problem by fitting the problem into the framework of maximum likelihood with missing data. The unknown correspondences are handled via mixture models. We derive a maximization criterion based on the expected complete-data log-likelihood. Then, the point registration problem can be solved by an instance of the expectation conditional maximization algorithm, that is, the ECMPR algorithm. Experiments with synthetic and real data are presented in each section. The proposed approaches provide satisfactory and promising results.
 
dc.description.naturepublished_or_final_version
 
dc.description.thesisdisciplineElectrical and Electronic Engineering
 
dc.description.thesisleveldoctoral
 
dc.description.thesisnameDoctor of Philosophy
 
dc.identifier.hkulb4807985
 
dc.languageeng
 
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)
 
dc.relation.ispartofHKU Theses Online (HKUTO)
 
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.source.urihttp://hub.hku.hk/bib/B48079856
 
dc.subject.lcshThree-dimensional imaging.
 
dc.subject.lcshImage reconstruction.
 
dc.subject.lcshImage processing.
 
dc.titleImage point matching in multiple-view object reconstruction from imagesequences
 
dc.typePG_Thesis
 
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<item><contributor.advisor>Chesi, G</contributor.advisor>
<contributor.advisor>Hung, YS</contributor.advisor>
<contributor.author>Zhang, Jian</contributor.author>
<contributor.author>&#24352;&#31616;</contributor.author>
<date.issued>2012</date.issued>
<description.abstract>&#65279;This thesis is concerned with three-dimensional (3D) reconstruction and point

registration, which are fundamental topics of numerous applications in the area of

computer vision.

First, we propose the multiple epipolar lines (MEL) shape recovery method for

3D reconstruction from an image sequence captured under circular motion. This

method involves recovering the 3D shape by reconstructing a set of 3D rim curves.

The position of each point on a 3D rim curve is estimated by using three or more

views. Two or more of these views are chosen close to each other to guarantee

good image point matching, while one or more views are chosen far from these

views to properly compensate for the error introduced in the triangulation scheme

by the short baseline of the close views. Image point matching among all views

is performed using a new method that suitably combines epipolar geometry and

cross-correlation.

Second, we develop the one line search (OLS) method for estimating the 3D

model of an object from a sequence of images. The recovered object comprises a

set of 3D rim curves. The OLS method determines the image point correspondences

of each 3D point through a single line search along the ray defined by the camera

center and each two-dimensional (2D) point where a photo-consistency index is

maximized. In accordance with the approach, the search area is independently reduced

to a line segment on the number of views. The key advantage of the proposed

method is that only one variable is focused on in defining the corresponding 3D

point, whereas the approaches for multiple-view stereo typically exploit multiple

epipolar lines and hence require multiple variables.

Third, we propose the expectation conditional maximization for point registration

(ECMPR) algorithm to solve the rigid point registration problem by fitting

the problem into the framework of maximum likelihood with missing data. The

unknown correspondences are handled via mixture models. We derive a maximization

criterion based on the expected complete-data log-likelihood. Then, the point

registration problem can be solved by an instance of the expectation conditional

maximization algorithm, that is, the ECMPR algorithm.

Experiments with synthetic and real data are presented in each section. The

proposed approaches provide satisfactory and promising results.</description.abstract>
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<publisher>The University of Hong Kong (Pokfulam, Hong Kong)</publisher>
<relation.ispartof>HKU Theses Online (HKUTO)</relation.ispartof>
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<rights>Creative Commons: Attribution 3.0 Hong Kong License</rights>
<source.uri>http://hub.hku.hk/bib/B48079856</source.uri>
<subject.lcsh>Three-dimensional imaging.</subject.lcsh>
<subject.lcsh>Image reconstruction.</subject.lcsh>
<subject.lcsh>Image processing.</subject.lcsh>
<title>Image point matching in multiple-view object reconstruction from imagesequences</title>
<type>PG_Thesis</type>
<identifier.hkul>b4807985</identifier.hkul>
<description.thesisname>Doctor of Philosophy</description.thesisname>
<description.thesislevel>doctoral</description.thesislevel>
<description.thesisdiscipline>Electrical and Electronic Engineering</description.thesisdiscipline>
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<date.hkucongregation>2012</date.hkucongregation>
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