Motion Detection Using Wavelet Analysis and Hierarchical Markov Models
This paper deals with the motion detection problem. This issue is of key importance in many application fields. To solve this problem, we compute the dominant motion in the sequence using a wavelet analysis and robust techniques. So, we obtain an estimation of the dominant motion on several image re...
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| creator | Demonceaux, Cédric Kachi-Akkouche, Djemâa |
| description | This paper deals with the motion detection problem. This issue is of key importance in many application fields. To solve this problem, we compute the dominant motion in the sequence using a wavelet analysis and robust techniques. So, we obtain an estimation of the dominant motion on several image resolutions. This method permits to define a hierarchical Markov model in a natural way. Thanks to this modelization, we overcome two problems: the solution sensibility in relation to the initial condition with a Markov random field, and the temporal aliasing. Moreover, we obtain a semi-iterative algorithm faster than using the multi-scale techniques. Thus, we introduce a fast and robust algorithm in order to compute the motion detection in an image sequence. This method is validated on real image sequences. |
| doi_str_mv | 10.1007/11676959_6 |
| format | Conference Proceeding |
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James</contributor><creatorcontrib>Demonceaux, Cédric ; Kachi-Akkouche, Djemâa ; MacLean, W. James</creatorcontrib><description>This paper deals with the motion detection problem. This issue is of key importance in many application fields. To solve this problem, we compute the dominant motion in the sequence using a wavelet analysis and robust techniques. So, we obtain an estimation of the dominant motion on several image resolutions. This method permits to define a hierarchical Markov model in a natural way. Thanks to this modelization, we overcome two problems: the solution sensibility in relation to the initial condition with a Markov random field, and the temporal aliasing. Moreover, we obtain a semi-iterative algorithm faster than using the multi-scale techniques. Thus, we introduce a fast and robust algorithm in order to compute the motion detection in an image sequence. This method is validated on real image sequences.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540325338</identifier><identifier>ISBN: 3540325336</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540325345</identifier><identifier>EISBN: 3540325344</identifier><identifier>DOI: 10.1007/11676959_6</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Coarse Scale ; Computer science; control theory; systems ; Exact sciences and technology ; Motion Compensation ; Motion Detection ; Motion Estimation ; Pattern recognition. Digital image processing. 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James</contributor><creatorcontrib>Demonceaux, Cédric</creatorcontrib><creatorcontrib>Kachi-Akkouche, Djemâa</creatorcontrib><title>Motion Detection Using Wavelet Analysis and Hierarchical Markov Models</title><title>Lecture notes in computer science</title><description>This paper deals with the motion detection problem. This issue is of key importance in many application fields. To solve this problem, we compute the dominant motion in the sequence using a wavelet analysis and robust techniques. So, we obtain an estimation of the dominant motion on several image resolutions. This method permits to define a hierarchical Markov model in a natural way. Thanks to this modelization, we overcome two problems: the solution sensibility in relation to the initial condition with a Markov random field, and the temporal aliasing. Moreover, we obtain a semi-iterative algorithm faster than using the multi-scale techniques. Thus, we introduce a fast and robust algorithm in order to compute the motion detection in an image sequence. This method is validated on real image sequences.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Coarse Scale</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Motion Compensation</subject><subject>Motion Detection</subject><subject>Motion Estimation</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Wavelet Analysis</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540325338</isbn><isbn>3540325336</isbn><isbn>9783540325345</isbn><isbn>3540325344</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpVUE1LAzEUjF9gqb34C3IRvKy-5OVjcyzVWqHFi8VjyO5m69p1tyRLof_eaAXxXWZgZh7DEHLN4I4B6HvGlFZGGqtOyMToHKUA5BKFPCUjphjLEIU5-6dhfk5GgMAzowVekkmMH5AOOTAJIzJf9UPTd_TBD778YevYdBv65va-9QOddq49xCZS11V00fjgQvnelK6lKxe2_Z6u-sq38Ypc1K6NfvKLY7KeP77OFtny5el5Nl1mO87yIZO1qV1lTA21K3xiqnKce-4KpXkhjYZcpK5eloVGnYPCshAKlBSVgZTAMbk5_t25mErUwXVlE-0uNJ8uHCwzBnODLPluj76YpG7jgy36fhstA_s9pf2bEr8Aa3pf3g</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Demonceaux, Cédric</creator><creator>Kachi-Akkouche, Djemâa</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Motion Detection Using Wavelet Analysis and Hierarchical Markov Models</title><author>Demonceaux, Cédric ; Kachi-Akkouche, Djemâa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p218t-5f9fad99f0fabead96da22e2ab672b597084302e5cb7378063cb460654d90abe3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Coarse Scale</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Motion Compensation</topic><topic>Motion Detection</topic><topic>Motion Estimation</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Wavelet Analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Demonceaux, Cédric</creatorcontrib><creatorcontrib>Kachi-Akkouche, Djemâa</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Demonceaux, Cédric</au><au>Kachi-Akkouche, Djemâa</au><au>MacLean, W. James</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Motion Detection Using Wavelet Analysis and Hierarchical Markov Models</atitle><btitle>Lecture notes in computer science</btitle><date>2006</date><risdate>2006</risdate><spage>64</spage><epage>75</epage><pages>64-75</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540325338</isbn><isbn>3540325336</isbn><eisbn>9783540325345</eisbn><eisbn>3540325344</eisbn><abstract>This paper deals with the motion detection problem. This issue is of key importance in many application fields. To solve this problem, we compute the dominant motion in the sequence using a wavelet analysis and robust techniques. So, we obtain an estimation of the dominant motion on several image resolutions. This method permits to define a hierarchical Markov model in a natural way. Thanks to this modelization, we overcome two problems: the solution sensibility in relation to the initial condition with a Markov random field, and the temporal aliasing. Moreover, we obtain a semi-iterative algorithm faster than using the multi-scale techniques. Thus, we introduce a fast and robust algorithm in order to compute the motion detection in an image sequence. This method is validated on real image sequences.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11676959_6</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2006, p.64-75 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19938931 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Coarse Scale Computer science control theory systems Exact sciences and technology Motion Compensation Motion Detection Motion Estimation Pattern recognition. Digital image processing. Computational geometry Wavelet Analysis |
| title | Motion Detection Using Wavelet Analysis and Hierarchical Markov Models |
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