Multi-View Active Shape Model with Robust Parameter Estimation
Active shape model is an efficient way for localizing objects with variable shapes. When ASM is extended to multi-view cases, the parameter estimation approaches in previous works are often sensitive to the initial view, as they do not handle the unreliability of local texture search, which can be c...
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| creator | Li Zhang Haizhou Ai |
| description | Active shape model is an efficient way for localizing objects with variable shapes. When ASM is extended to multi-view cases, the parameter estimation approaches in previous works are often sensitive to the initial view, as they do not handle the unreliability of local texture search, which can be caused by bad initialization or cluttered background. To overcome this problem, we propose a novel algorithm for parameter estimation, using robust estimators to remove outliers. By weighting dynamically, our method acts as a model selection method, which reveals the hidden shape and view parameters from noisy observations of local texture models. Experiments and comparisons on multi-view face alignment are carried out to show the efficiency of our approach |
| doi_str_mv | 10.1109/ICPR.2006.834 |
| format | Conference Proceeding |
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When ASM is extended to multi-view cases, the parameter estimation approaches in previous works are often sensitive to the initial view, as they do not handle the unreliability of local texture search, which can be caused by bad initialization or cluttered background. To overcome this problem, we propose a novel algorithm for parameter estimation, using robust estimators to remove outliers. By weighting dynamically, our method acts as a model selection method, which reveals the hidden shape and view parameters from noisy observations of local texture models. Experiments and comparisons on multi-view face alignment are carried out to show the efficiency of our approach</description><identifier>ISSN: 1051-4651</identifier><identifier>ISBN: 0769525210</identifier><identifier>ISBN: 9780769525211</identifier><identifier>EISSN: 2831-7475</identifier><identifier>DOI: 10.1109/ICPR.2006.834</identifier><language>eng</language><publisher>IEEE</publisher><subject>Active shape model ; Computer science ; Humans ; Multi-stage noise shaping ; Parameter estimation ; Principal component analysis ; Robustness ; Solid modeling ; Statistical distributions ; Training data</subject><ispartof>18th International Conference on Pattern Recognition (ICPR'06), 2006, Vol.4, p.469-468</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1699880$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,4051,4052,27930,54925</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1699880$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Li Zhang</creatorcontrib><creatorcontrib>Haizhou Ai</creatorcontrib><title>Multi-View Active Shape Model with Robust Parameter Estimation</title><title>18th International Conference on Pattern Recognition (ICPR'06)</title><addtitle>ICPR</addtitle><description>Active shape model is an efficient way for localizing objects with variable shapes. When ASM is extended to multi-view cases, the parameter estimation approaches in previous works are often sensitive to the initial view, as they do not handle the unreliability of local texture search, which can be caused by bad initialization or cluttered background. To overcome this problem, we propose a novel algorithm for parameter estimation, using robust estimators to remove outliers. By weighting dynamically, our method acts as a model selection method, which reveals the hidden shape and view parameters from noisy observations of local texture models. Experiments and comparisons on multi-view face alignment are carried out to show the efficiency of our approach</description><subject>Active shape model</subject><subject>Computer science</subject><subject>Humans</subject><subject>Multi-stage noise shaping</subject><subject>Parameter estimation</subject><subject>Principal component analysis</subject><subject>Robustness</subject><subject>Solid modeling</subject><subject>Statistical distributions</subject><subject>Training data</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>0769525210</isbn><isbn>9780769525211</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjctOwzAQAC0eEqH0yImLf8Bl1_bG8QWpigpUakVVHtfKThzVKCVV4lLx91SCucxthrFbhAki2Pt5uVpPJEA-KZQ-Y5ksFAqjDZ2zazC5JUkS4YJlCIRC54RXbDwMn3BCE2lpM_awPLQpio8Yjnxapfgd-OvW7QNfdnVo-TGmLV93_jAkvnK924UUej4bUty5FLuvG3bZuHYI43-P2Pvj7K18FouXp3k5XYiIhpLwqqlrmXuqGqNJN7UzaJ2z5BQ0HiQ05Arlna6UUbVWJlQFaY8BQXmToxqxu79uDCFs9v1p3_9sMLe2KED9AiD2SaY</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Li Zhang</creator><creator>Haizhou Ai</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2006</creationdate><title>Multi-View Active Shape Model with Robust Parameter Estimation</title><author>Li Zhang ; Haizhou Ai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b3fdd26b5cf7454fda719aa95a30fb020f5a83ba4c373d437ec854b1e103b7613</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Active shape model</topic><topic>Computer science</topic><topic>Humans</topic><topic>Multi-stage noise shaping</topic><topic>Parameter estimation</topic><topic>Principal component analysis</topic><topic>Robustness</topic><topic>Solid modeling</topic><topic>Statistical distributions</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Li Zhang</creatorcontrib><creatorcontrib>Haizhou Ai</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li Zhang</au><au>Haizhou Ai</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multi-View Active Shape Model with Robust Parameter Estimation</atitle><btitle>18th International Conference on Pattern Recognition (ICPR'06)</btitle><stitle>ICPR</stitle><date>2006</date><risdate>2006</risdate><volume>4</volume><spage>469</spage><epage>468</epage><pages>469-468</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>0769525210</isbn><isbn>9780769525211</isbn><abstract>Active shape model is an efficient way for localizing objects with variable shapes. When ASM is extended to multi-view cases, the parameter estimation approaches in previous works are often sensitive to the initial view, as they do not handle the unreliability of local texture search, which can be caused by bad initialization or cluttered background. To overcome this problem, we propose a novel algorithm for parameter estimation, using robust estimators to remove outliers. By weighting dynamically, our method acts as a model selection method, which reveals the hidden shape and view parameters from noisy observations of local texture models. Experiments and comparisons on multi-view face alignment are carried out to show the efficiency of our approach</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2006.834</doi><tpages>0</tpages></addata></record> |
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| ispartof | 18th International Conference on Pattern Recognition (ICPR'06), 2006, Vol.4, p.469-468 |
| issn | 1051-4651 2831-7475 |
| language | eng |
| recordid | cdi_ieee_primary_1699880 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Active shape model Computer science Humans Multi-stage noise shaping Parameter estimation Principal component analysis Robustness Solid modeling Statistical distributions Training data |
| title | Multi-View Active Shape Model with Robust Parameter Estimation |
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