{"id":"https://openalex.org/W2132110568","doi":"https://doi.org/10.1109/icpr.2008.4761516","title":"Direct 3-D shape recovery from image sequence based on multi-scale Bayesian network","display_name":"Direct 3-D shape recovery from image sequence based on multi-scale Bayesian network","publication_year":2008,"publication_date":"2008-12-01","ids":{"openalex":"https://openalex.org/W2132110568","doi":"https://doi.org/10.1109/icpr.2008.4761516","mag":"2132110568"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2008.4761516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2008.4761516","pdf_url":null,"source":{"id":"https://openalex.org/S4393916651","display_name":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","issn_l":"1041-3278","issn":["1041-3278","1051-4651"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 19th International Conference on Pattern Recognition","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103190397","display_name":"Norio Tagawa","orcid":null},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Norio Tagawa","raw_affiliation_strings":["Tokyo Metropolitan University, Hino, Tokyo, Japan","Tokyo Metropolitan Univ., Hino"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University, Hino, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]},{"raw_affiliation_string":"Tokyo Metropolitan Univ., Hino","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087455386","display_name":"Junya Kawaguchi","orcid":null},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Junya Kawaguchi","raw_affiliation_strings":["Tokyo Metropolitan University, Hino, Tokyo, Japan","Tokyo Metropolitan Univ., Hino"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University, Hino, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]},{"raw_affiliation_string":"Tokyo Metropolitan Univ., Hino","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073850573","display_name":"Shoichi Naganuma","orcid":null},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shoichi Naganuma","raw_affiliation_strings":["Tokyo Metropolitan University, Hino, Tokyo, Japan","Tokyo Metropolitan Univ., Hino"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University, Hino, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]},{"raw_affiliation_string":"Tokyo Metropolitan Univ., Hino","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039273761","display_name":"Kan Okubo","orcid":"https://orcid.org/0000-0002-2924-7811"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kan Okubo","raw_affiliation_strings":["Tokyo Metropolitan University, Hino, Tokyo, Japan","Tokyo Metropolitan Univ., Hino"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University, Hino, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]},{"raw_affiliation_string":"Tokyo Metropolitan Univ., Hino","institution_ids":["https://openalex.org/I69740276"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103190397"],"corresponding_institution_ids":["https://openalex.org/I69740276"],"apc_list":null,"apc_paid":null,"fwci":0.4316,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.51766595,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10638","display_name":"Optical measurement and interference techniques","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.8362334966659546},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.803276538848877},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5764318108558655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5577598810195923},{"id":"https://openalex.org/keywords/belief-propagation","display_name":"Belief propagation","score":0.557093620300293},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5454710721969604},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5434172749519348},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.511671781539917},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4851565957069397},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.485043466091156},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4571208655834198},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4547913670539856},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44934141635894775},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4325176775455475},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4251898229122162},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4199141561985016},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.41243332624435425},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3875531554222107},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31042975187301636},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.29808998107910156},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.13688525557518005},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07864198088645935}],"concepts":[{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.8362334966659546},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.803276538848877},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5764318108558655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5577598810195923},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.557093620300293},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5454710721969604},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5434172749519348},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.511671781539917},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4851565957069397},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.485043466091156},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4571208655834198},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4547913670539856},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44934141635894775},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4325176775455475},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4251898229122162},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4199141561985016},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.41243332624435425},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3875531554222107},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31042975187301636},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.29808998107910156},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.13688525557518005},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07864198088645935},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icpr.2008.4761516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2008.4761516","pdf_url":null,"source":{"id":"https://openalex.org/S4393916651","display_name":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","issn_l":"1041-3278","issn":["1041-3278","1051-4651"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 19th International Conference on Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.214.3309","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.3309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://figment.cse.usf.edu/~sfefilat/data/papers/TuAT9.46.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1489082385","https://openalex.org/W1867429401","https://openalex.org/W1961031432","https://openalex.org/W2049633694","https://openalex.org/W2154504070","https://openalex.org/W2308181013","https://openalex.org/W4249022109","https://openalex.org/W6629246234","https://openalex.org/W6639126518"],"related_works":["https://openalex.org/W2169282664","https://openalex.org/W2396038226","https://openalex.org/W4293094099","https://openalex.org/W1783992599","https://openalex.org/W2114899076","https://openalex.org/W2097090565","https://openalex.org/W2516546424","https://openalex.org/W2151646056","https://openalex.org/W2008444830","https://openalex.org/W2180905035"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,7,32,37,42,47,58,73,83],"new":[3],"method":[4],"for":[5],"recovering":[6],"3-D":[8,55],"object":[9,62],"shape":[10],"from":[11],"an":[12,61],"image":[13],"sequence.":[14],"In":[15],"order":[16],"to":[17,81],"recover":[18],"high-resolution":[19],"relative":[20,54,68],"depth":[21],"without":[22],"using":[23,46],"the":[24,71],"complex":[25],"Markov":[26],"random":[27],"field":[28],"(MRF)":[29],"that":[30],"includes":[31],"line":[33],"process,":[34],"we":[35],"construct":[36],"recovery":[38],"algorithm":[39,77],"based":[40],"on":[41],"belief":[43],"propagation":[44],"scheme":[45],"multi-scale":[48],"Bayesian":[49],"network.":[50],"With":[51],"this":[52],"algorithm,":[53],"motion":[56],"between":[57],"camera":[59],"and":[60,70],"can":[63],"be":[64],"determined":[65],"together":[66],"with":[67],"depth,":[69],"maximum":[72],"posteriori":[74],"expectation-maximization":[75],"(MAP-EM)":[76],"is":[78],"effectively":[79],"used":[80],"determine":[82],"suitable":[84],"approximation.":[85]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
