Component optimization for image understanding: a Bayesian approach
In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in great...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2006-05, Vol.28 (5), p.684-693 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Li Cheng Caelli, T. Sanchez-Azofeifa, A. |
| description | In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the application of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems. |
| doi_str_mv | 10.1109/TPAMI.2006.92 |
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This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the application of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>DOI: 10.1109/TPAMI.2006.92</identifier><identifier>PMID: 16640256</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>3D fitting ; Agronomy. Soil science and plant productions ; Algorithms ; Annotations ; Applied sciences ; Artificial Intelligence ; Bayes Theorem ; Bayesian analysis ; Bayesian methods ; Biological and medical sciences ; Computer science; control theory; systems ; Delay estimation ; Exact sciences and technology ; Fittings ; Forestry ; forestry inventory ; Fundamental and applied biological sciences. Psychology ; General agronomy. Plant production ; Geometry ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image reconstruction ; Image segmentation ; image understanding ; Imaging, Three-Dimensional - methods ; Information Storage and Retrieval - methods ; Labeling ; Layout ; Models, Statistical ; Optimization ; Pattern Recognition, Automated - methods ; Pattern recognition. Digital image processing. Computational geometry ; Robustness ; scene analysis ; Segmentation ; Statistical learning ; stereo ; Three dimensional ; Trees</subject><ispartof>IEEE transactions on pattern analysis and machine intelligence, 2006-05, Vol.28 (5), p.684-693</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c431t-46cdda73c01eba9ac722849857307c8f69640ff66e351637d8e2332bd2c7fc843</citedby><cites>FETCH-LOGICAL-c431t-46cdda73c01eba9ac722849857307c8f69640ff66e351637d8e2332bd2c7fc843</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1608033$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1608033$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17676189$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16640256$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li Cheng</creatorcontrib><creatorcontrib>Caelli, T.</creatorcontrib><creatorcontrib>Sanchez-Azofeifa, A.</creatorcontrib><title>Component optimization for image understanding: a Bayesian approach</title><title>IEEE transactions on pattern analysis and machine intelligence</title><addtitle>TPAMI</addtitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><description>In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the application of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems.</description><subject>3D fitting</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Algorithms</subject><subject>Annotations</subject><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian methods</subject><subject>Biological and medical sciences</subject><subject>Computer science; control theory; systems</subject><subject>Delay estimation</subject><subject>Exact sciences and technology</subject><subject>Fittings</subject><subject>Forestry</subject><subject>forestry inventory</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agronomy. Plant production</subject><subject>Geometry</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image reconstruction</subject><subject>Image segmentation</subject><subject>image understanding</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Information Storage and Retrieval - methods</subject><subject>Labeling</subject><subject>Layout</subject><subject>Models, Statistical</subject><subject>Optimization</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Robustness</subject><subject>scene analysis</subject><subject>Segmentation</subject><subject>Statistical learning</subject><subject>stereo</subject><subject>Three dimensional</subject><subject>Trees</subject><issn>0162-8828</issn><issn>1939-3539</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0T1vFDEQBmALgcgRKKmQ0AqJUO0x_tixTRdOASIFQRFqy-f1Bke39mLvFuHX4-NOCqKAys2jdzzzEvKcwppS0G-vv55_vlwzAFxr9oCsqOa65R3XD8kKKLJWKaZOyJNSbgGo6IA_JicUUQDrcEU2mzROKfo4N2mawxh-2jmk2AwpN2G0N75ZYu9zmW3sQ7x519jmvb3zJdjY2GnKybrvT8mjwe6Kf3Z8T8m3DxfXm0_t1ZePl5vzq9YJTudWoOt7K7kD6rdWWycZU0KrTnKQTg2o66eGAdHzjiKXvfKMc7btmZODU4KfkjeH3Dr2x-LLbMZQnN_tbPRpKUZpZECllFWe_VOi1LQeA_4LmdRaaIEVvvoL3qYlx7quUVg3EIB71B6Qy6mU7Acz5XrEfGcomH1b5ndbZt-W0az6l8fQZTv6_l4f66ng9RHY4uxuyDa6UO6dRIlU6epeHFzw3v8RAwo4578APaujkw</recordid><startdate>20060501</startdate><enddate>20060501</enddate><creator>Li Cheng</creator><creator>Caelli, T.</creator><creator>Sanchez-Azofeifa, A.</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20060501</creationdate><title>Component optimization for image understanding: a Bayesian approach</title><author>Li Cheng ; Caelli, T. ; Sanchez-Azofeifa, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c431t-46cdda73c01eba9ac722849857307c8f69640ff66e351637d8e2332bd2c7fc843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>3D fitting</topic><topic>Agronomy. Soil science and plant productions</topic><topic>Algorithms</topic><topic>Annotations</topic><topic>Applied sciences</topic><topic>Artificial Intelligence</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Bayesian methods</topic><topic>Biological and medical sciences</topic><topic>Computer science; control theory; systems</topic><topic>Delay estimation</topic><topic>Exact sciences and technology</topic><topic>Fittings</topic><topic>Forestry</topic><topic>forestry inventory</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General agronomy. Plant production</topic><topic>Geometry</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image reconstruction</topic><topic>Image segmentation</topic><topic>image understanding</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Information Storage and Retrieval - methods</topic><topic>Labeling</topic><topic>Layout</topic><topic>Models, Statistical</topic><topic>Optimization</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Robustness</topic><topic>scene analysis</topic><topic>Segmentation</topic><topic>Statistical learning</topic><topic>stereo</topic><topic>Three dimensional</topic><topic>Trees</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li Cheng</creatorcontrib><creatorcontrib>Caelli, T.</creatorcontrib><creatorcontrib>Sanchez-Azofeifa, A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li Cheng</au><au>Caelli, T.</au><au>Sanchez-Azofeifa, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Component optimization for image understanding: a Bayesian approach</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2006-05-01</date><risdate>2006</risdate><volume>28</volume><issue>5</issue><spage>684</spage><epage>693</epage><pages>684-693</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><coden>ITPIDJ</coden><abstract>In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the application of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems.</abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><pmid>16640256</pmid><doi>10.1109/TPAMI.2006.92</doi><tpages>10</tpages></addata></record> |
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| subjects | 3D fitting Agronomy. Soil science and plant productions Algorithms Annotations Applied sciences Artificial Intelligence Bayes Theorem Bayesian analysis Bayesian methods Biological and medical sciences Computer science control theory systems Delay estimation Exact sciences and technology Fittings Forestry forestry inventory Fundamental and applied biological sciences. Psychology General agronomy. Plant production Geometry Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image reconstruction Image segmentation image understanding Imaging, Three-Dimensional - methods Information Storage and Retrieval - methods Labeling Layout Models, Statistical Optimization Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Robustness scene analysis Segmentation Statistical learning stereo Three dimensional Trees |
| title | Component optimization for image understanding: a Bayesian approach |
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