Lip Localization Based on Active Shape Model and Gaussian Mixture Model
This paper describes an efficient method for locating lip. Lip deformation is modeled by a statistically deformable model based on Active Shape Model(ASM). In ASM based methods, it is assumed that a training set forms a cluster in shape parameter space. However if there are some clusters in shape pa...
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| creator | Jang, Kyung Shik Han, Soowhan Lee, Imgeun Woo, Young Woon |
| description | This paper describes an efficient method for locating lip. Lip deformation is modeled by a statistically deformable model based on Active Shape Model(ASM). In ASM based methods, it is assumed that a training set forms a cluster in shape parameter space. However if there are some clusters in shape parameter space due to an incorrect position of landmark point, ASM may not be able to locate new examples accurately. In this paper, Gaussian mixture is used to characterize the distribution of shape parameter. The Expectation Maximization algorithm is used to determine the maximum likelihood parameters of Gaussian mixture. During search, we resolved the updated locations by projecting a shape into the shape parameter space by using Gaussian mixture. The experiment was performed on many images, and showed very encouraging result. |
| doi_str_mv | 10.1007/11949534_105 |
| format | Conference Proceeding |
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Lip deformation is modeled by a statistically deformable model based on Active Shape Model(ASM). In ASM based methods, it is assumed that a training set forms a cluster in shape parameter space. However if there are some clusters in shape parameter space due to an incorrect position of landmark point, ASM may not be able to locate new examples accurately. In this paper, Gaussian mixture is used to characterize the distribution of shape parameter. The Expectation Maximization algorithm is used to determine the maximum likelihood parameters of Gaussian mixture. During search, we resolved the updated locations by projecting a shape into the shape parameter space by using Gaussian mixture. The experiment was performed on many images, and showed very encouraging result.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540682974</identifier><identifier>ISBN: 354068297X</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540682988</identifier><identifier>EISBN: 9783540682981</identifier><identifier>DOI: 10.1007/11949534_105</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Active Shape Model ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Deformable Model ; Exact sciences and technology ; Gaussian Mixture Model ; Landmark Point ; Pattern recognition. Digital image processing. Computational geometry ; Shape Parameter</subject><ispartof>Advances in Image and Video Technology, 2006, p.1049-1058</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><rights>2007 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c263t-8fd49d0620dec52db471a5492e2aff0dfe1f75a826382a692cd0a7b7e932ed303</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11949534_105$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11949534_105$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,779,780,784,789,790,793,4050,4051,27925,38255,41442,42511</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19008313$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Lie, Wen-Nung</contributor><contributor>Chang, Long-Wen</contributor><creatorcontrib>Jang, Kyung Shik</creatorcontrib><creatorcontrib>Han, Soowhan</creatorcontrib><creatorcontrib>Lee, Imgeun</creatorcontrib><creatorcontrib>Woo, Young Woon</creatorcontrib><title>Lip Localization Based on Active Shape Model and Gaussian Mixture Model</title><title>Advances in Image and Video Technology</title><description>This paper describes an efficient method for locating lip. Lip deformation is modeled by a statistically deformable model based on Active Shape Model(ASM). In ASM based methods, it is assumed that a training set forms a cluster in shape parameter space. However if there are some clusters in shape parameter space due to an incorrect position of landmark point, ASM may not be able to locate new examples accurately. In this paper, Gaussian mixture is used to characterize the distribution of shape parameter. The Expectation Maximization algorithm is used to determine the maximum likelihood parameters of Gaussian mixture. During search, we resolved the updated locations by projecting a shape into the shape parameter space by using Gaussian mixture. The experiment was performed on many images, and showed very encouraging result.</description><subject>Active Shape Model</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Deformable Model</subject><subject>Exact sciences and technology</subject><subject>Gaussian Mixture Model</subject><subject>Landmark Point</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Shape Parameter</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540682974</isbn><isbn>354068297X</isbn><isbn>3540682988</isbn><isbn>9783540682981</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkMtOwzAQRc1LopTu-ABv2CAFxo_E9rJUUJBSsQDW0TS2wVCSKE4r4OsxapGYzVzpHI1Gl5AzBpcMQF0xZqTJhawY5HvkROQSCs2N1vtkxArGMiGkOSATo_QfU_KQjEAAz1IUx2QS4xukESw3mo3IvAwdLdsaV-Ebh9A29BqjszSFaT2EjaOPr9g5umitW1FsLJ3jOsaADV2Ez2Hd79ApOfK4im6y22PyfHvzNLvLyof5_WxaZjUvxJBpb6WxUHCwrs65XUrFMJeGO47eg_WOeZWjTrLmWBheW0C1VM4I7qwAMSbn27sdxvS077GpQ6y6Pnxg_1UxA6AFE8m72HoxoebF9dWybd9jaq76rbL6X6X4AZODX4s</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Jang, Kyung Shik</creator><creator>Han, Soowhan</creator><creator>Lee, Imgeun</creator><creator>Woo, Young Woon</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Lip Localization Based on Active Shape Model and Gaussian Mixture Model</title><author>Jang, Kyung Shik ; Han, Soowhan ; Lee, Imgeun ; Woo, Young Woon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c263t-8fd49d0620dec52db471a5492e2aff0dfe1f75a826382a692cd0a7b7e932ed303</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Active Shape Model</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Deformable Model</topic><topic>Exact sciences and technology</topic><topic>Gaussian Mixture Model</topic><topic>Landmark Point</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Shape Parameter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jang, Kyung Shik</creatorcontrib><creatorcontrib>Han, Soowhan</creatorcontrib><creatorcontrib>Lee, Imgeun</creatorcontrib><creatorcontrib>Woo, Young Woon</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jang, Kyung Shik</au><au>Han, Soowhan</au><au>Lee, Imgeun</au><au>Woo, Young Woon</au><au>Lie, Wen-Nung</au><au>Chang, Long-Wen</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Lip Localization Based on Active Shape Model and Gaussian Mixture Model</atitle><btitle>Advances in Image and Video Technology</btitle><date>2006</date><risdate>2006</risdate><spage>1049</spage><epage>1058</epage><pages>1049-1058</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540682974</isbn><isbn>354068297X</isbn><eisbn>3540682988</eisbn><eisbn>9783540682981</eisbn><abstract>This paper describes an efficient method for locating lip. Lip deformation is modeled by a statistically deformable model based on Active Shape Model(ASM). In ASM based methods, it is assumed that a training set forms a cluster in shape parameter space. However if there are some clusters in shape parameter space due to an incorrect position of landmark point, ASM may not be able to locate new examples accurately. In this paper, Gaussian mixture is used to characterize the distribution of shape parameter. The Expectation Maximization algorithm is used to determine the maximum likelihood parameters of Gaussian mixture. During search, we resolved the updated locations by projecting a shape into the shape parameter space by using Gaussian mixture. The experiment was performed on many images, and showed very encouraging result.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11949534_105</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Image and Video Technology, 2006, p.1049-1058 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19008313 |
| source | Springer Books |
| subjects | Active Shape Model Applied sciences Artificial intelligence Computer science control theory systems Deformable Model Exact sciences and technology Gaussian Mixture Model Landmark Point Pattern recognition. Digital image processing. Computational geometry Shape Parameter |
| title | Lip Localization Based on Active Shape Model and Gaussian Mixture Model |
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