Joint Non-rigid Motion Estimation and Segmentation
Result description
Usually, object segmentation and motion estimation are considered (and modelled) as different tasks. For motion estimation this leads to problems arising especially at the boundary of an object moving in front of another if e.g. prior assumptions about continuity of the motion field are made. Thus we expect that a good segmentation will improve the motion estimation and vice versa. To demonstrate this, we consider the simple task of joint segmentation and motion estimation of an arbitrary (non-rigid) object moving in front of a still background. We propose a statistical model which represents the moving object as a triangular mesh of pairs of corresponding points and introduce an provably correct iterative scheme, which simultaneously finds the optimalsegmentation and corresponding motion field.
Keywords
Computer visionMarkov random fieldsmotion estimationsegmentation
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Joint Non-rigid Motion Estimation and Segmentation
Original language description
Usually, object segmentation and motion estimation are considered (and modelled) as different tasks. For motion estimation this leads to problems arising especially at the boundary of an object moving in front of another if e.g. prior assumptions about continuity of the motion field are made. Thus we expect that a good segmentation will improve the motion estimation and vice versa. To demonstrate this, we consider the simple task of joint segmentation and motion estimation of an arbitrary (non-rigid) object moving in front of a still background. We propose a statistical model which represents the moving object as a triangular mesh of pairs of corresponding points and introduce an provably correct iterative scheme, which simultaneously finds the optimalsegmentation and corresponding motion field.
Czech name
Není k dispozici
Czech description
Není k dispozici
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
1ET101210406: Automatic 3D Virtual Model Builder from Photographs
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IWCIA '04: Proceedings 10th International Workshop on Combinatorial Image Analysis
ISBN
0302-9743
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
631-638
Publisher name
Springer
Place of publication
Heidelberg
Event location
Auckland
Event date
Dec 1, 2004
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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Basic information
Result type
D - Article in proceedings
CEP
JD - Use of computers, robotics and its application
Year of implementation
2004