Linked Life Data

19696452

Source:http://linkedlifedata.com/resource/pubmed/id/19696452

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2009-8-21
pubmed:abstractText
Elastic motion is a nonrigid motion constrained only by some degree of smoothness and continuity. Consequently, elastic motion estimation by explicit feature matching actually contains two correlated subproblems: shape registration and motion tracking, which account for spatial smoothness and temporal continuity, respectively. If we ignore their interrelationship, solving each of them alone will be rather challenging, especially when the cluttered features are involved. To integrate them into a probabilistic model, one straightforward approach is to draw the dependence between their hidden states. With regard to their separated states, there are, however, two different explanations of motion which are still made under the individual constraint of smoothness or continuity. Each one can be error-prone, and their coupling causes error propagation. Therefore, it is highly desirable to design a probabilistic model in which a unified state is shared by the two subproblems. This paper is intended to propose such a model, i.e., a Mixture of Transformed Hidden Markov Models (MTHMM), where a unique explanation of motion is made simultaneously under the spatiotemporal constraints. As a result, the MTHMM could find a coherent global interpretation of elastic motion from local cluttered edge features, and experiments show its robustness under ambiguities, data missing, and outliers.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0162-8828
pubmed:author
pubmed:issnType
Print
pubmed:volume
31
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1817-30
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A Mixture of Transformed Hidden Markov Models for elastic motion estimation.
pubmed:affiliation
Department of Computer Science and Technology, Tsinghua University, Beijing, People's Republic of China. dhj98@mails.tsinghua.edu.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't