{"id":"https://openalex.org/W2096663039","doi":"https://doi.org/10.1109/tce.2006.1649675","title":"Accurate Estimation of Missing Data under Noise Distribution","display_name":"Accurate Estimation of Missing Data under Noise Distribution","publication_year":2006,"publication_date":"2006-05-01","ids":{"openalex":"https://openalex.org/W2096663039","doi":"https://doi.org/10.1109/tce.2006.1649675","mag":"2096663039"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2006.1649675","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2006.1649675","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-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/A5020230628","display_name":"Sung-shik Koh","orcid":null},"institutions":[{"id":"https://openalex.org/I317356780","display_name":"Osaka City University","ror":"https://ror.org/02skfsw40","country_code":"JP","type":"education","lineage":["https://openalex.org/I317356780"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sung-Shik Koh","raw_affiliation_strings":["Graduate School of Engineering, Osaka City University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Osaka City University, Japan","institution_ids":["https://openalex.org/I317356780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091085003","display_name":"H. Hama","orcid":"https://orcid.org/0000-0001-8905-0888"},"institutions":[{"id":"https://openalex.org/I317356780","display_name":"Osaka City University","ror":"https://ror.org/02skfsw40","country_code":"JP","type":"education","lineage":["https://openalex.org/I317356780"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"H. Hama","raw_affiliation_strings":["Graduate School of Engineering, Osaka City University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Osaka City University, Japan","institution_ids":["https://openalex.org/I317356780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058861287","display_name":"Thi Thi Zin","orcid":null},"institutions":[{"id":"https://openalex.org/I317356780","display_name":"Osaka City University","ror":"https://ror.org/02skfsw40","country_code":"JP","type":"education","lineage":["https://openalex.org/I317356780"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"T.T. Zin","raw_affiliation_strings":["Graduate School of Engineering, Osaka City University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Osaka City University, Japan","institution_ids":["https://openalex.org/I317356780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I317356780"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14690484,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"52","issue":"2","first_page":"528","last_page":"535"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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":1.0,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9997000098228455,"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":0.9991999864578247,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.8332046866416931},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.7544274926185608},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6341809630393982},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6121541261672974},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.555436372756958},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5187361836433411},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4511524736881256},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.43726491928100586},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.4252236485481262},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3753651976585388},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3622799515724182},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27449285984039307},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17903196811676025}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8332046866416931},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.7544274926185608},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6341809630393982},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6121541261672974},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.555436372756958},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5187361836433411},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4511524736881256},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.43726491928100586},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.4252236485481262},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3753651976585388},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3622799515724182},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27449285984039307},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17903196811676025},{"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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2006.1649675","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2006.1649675","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1493931800","https://openalex.org/W1499236264","https://openalex.org/W1514635661","https://openalex.org/W1516172206","https://openalex.org/W1535312509","https://openalex.org/W1988593557","https://openalex.org/W2000321612","https://openalex.org/W2005929595","https://openalex.org/W2008142333","https://openalex.org/W2088284286","https://openalex.org/W2096330980","https://openalex.org/W2111308925","https://openalex.org/W2117083552","https://openalex.org/W2129415393","https://openalex.org/W2135960718","https://openalex.org/W2138062876","https://openalex.org/W2138835141","https://openalex.org/W2146201632","https://openalex.org/W2149014128","https://openalex.org/W2151812852","https://openalex.org/W2162373368","https://openalex.org/W2170245698","https://openalex.org/W2798909945","https://openalex.org/W4231149835","https://openalex.org/W6629707539","https://openalex.org/W6630923564","https://openalex.org/W6674458952","https://openalex.org/W6676770471","https://openalex.org/W6677334830","https://openalex.org/W6684002151"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2539013788","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2951579230","https://openalex.org/W2613627778"],"abstract_inverted_index":{"3D":[0,158],"contents":[1,59],"have":[2,67],"been":[3,69],"becoming":[4],"one":[5],"of":[6,37,53,64,94,101,131,138,164,203],"attractive":[7],"multimedia":[8],"and":[9,157,176,194,201],"reality.":[10],"In":[11,50,86],"computer":[12],"vision,":[13],"2D-to-3D":[14],"conversion":[15],"techniques":[16],"require":[17],"estimating":[18,171],"missing":[19,28,84,95,144,172],"data":[20,29,96,145],"from":[21,108],"noisy":[22],"observations.":[23,110],"When":[24],"there":[25],"is":[26,40,80,114,141],"no":[27,75],"in":[30,186],"the":[31,34,51,65,72,99,124,132,178,199,204],"observation":[32,133],"matrix,":[33],"accurate":[35,184],"solution":[36],"such":[38],"problem":[39,73],"known":[41],"to":[42,60,82,142],"be":[43],"given":[44,127],"by":[45,151],"singular":[46],"value":[47],"decomposition":[48],"(SVD).":[49],"case":[52],"converting":[54],"already":[55],"recorded":[56],"monoscopic":[57],"video":[58,181,196],"3D,":[61],"several":[62],"entries":[63],"matrix":[66],"not":[68],"observed.":[70],"Therefore,":[71],"has":[74],"simple":[76],"solution,":[77],"so":[78],"it":[79],"necessary":[81],"estimate":[83,123,143],"data.":[85],"this":[87],"paper,":[88],"we":[89],"propose":[90],"an":[91,115,128],"estimation":[92,174],"algorithm":[93,140],"with":[97,192],"minimizing":[98],"influence":[100],"noise":[102,149],"embedded":[103],"when":[104],"tracking":[105],"feature":[106],"points":[107],"partial":[109],"The":[111,135,183],"proposed":[112,205],"method":[113,120],"iterative":[116],"affine":[117],"SVD":[118],"factorization":[119],"which":[121],"can":[122],"model":[125],"parameters,":[126],"incomplete":[129],"set":[130],"matrix.":[134],"main":[136,166],"idea":[137],"our":[139],"accurately":[146],"even":[147],"under":[148],"distribution":[150],"using":[152],"geometrical":[153,168],"correlations":[154,169],"between":[155],"2D":[156],"error":[159],"space.":[160],"This":[161],"paper":[162],"consists":[163],"three":[165],"phases:":[167],"for":[170,180],"data,":[173],"algorithm,":[175],"analyzing":[177],"results":[179,185],"sequences.":[182],"practical":[187],"situations":[188],"as":[189],"demonstrated":[190],"here":[191],"synthetic":[193],"real":[195],"sequences":[197],"show":[198],"efficiency":[200],"flexibility":[202],"method.":[206]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}