{"id":"https://openalex.org/W2417143499","doi":"https://doi.org/10.1109/acpr.2015.7486578","title":"Semi-global depth from focus","display_name":"Semi-global depth from focus","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2417143499","doi":"https://doi.org/10.1109/acpr.2015.7486578","mag":"2417143499"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486578","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486578","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5100451376","display_name":"Wentao Liu","orcid":"https://orcid.org/0000-0001-6587-9878"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wentao Liu","raw_affiliation_strings":["Key Laboratory of Machine Perception and Intelligence, Speech and Hearing Research Center, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception and Intelligence, Speech and Hearing Research Center, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101171443","display_name":"Xihong Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xihong Wu","raw_affiliation_strings":["Key Laboratory of Machine Perception and Intelligence, Speech and Hearing Research Center, Peking University, Beijing, Beijing, CN"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception and Intelligence, Speech and Hearing Research Center, Peking University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100451376"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.30300965,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"624","last_page":"629"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9302999973297119,"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":0.9129999876022339,"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/robustness","display_name":"Robustness (evolution)","score":0.7030943632125854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6470340490341187},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6401787996292114},{"id":"https://openalex.org/keywords/classification-of-discontinuities","display_name":"Classification of discontinuities","score":0.6222259998321533},{"id":"https://openalex.org/keywords/laplace-operator","display_name":"Laplace operator","score":0.5625860691070557},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5316712856292725},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5270662307739258},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49610981345176697},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.4630228877067566},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4418322443962097},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4132794141769409},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3527478873729706},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3177133798599243},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26541411876678467},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.14317402243614197}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7030943632125854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6470340490341187},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6401787996292114},{"id":"https://openalex.org/C15627037","wikidata":"https://www.wikidata.org/wiki/Q541961","display_name":"Classification of discontinuities","level":2,"score":0.6222259998321533},{"id":"https://openalex.org/C165700671","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Laplace operator","level":2,"score":0.5625860691070557},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5316712856292725},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5270662307739258},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49610981345176697},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.4630228877067566},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4418322443962097},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4132794141769409},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3527478873729706},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3177133798599243},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26541411876678467},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.14317402243614197},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2015.7486578","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486578","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1655654231","https://openalex.org/W1965357857","https://openalex.org/W1965475047","https://openalex.org/W1977442750","https://openalex.org/W1996126470","https://openalex.org/W2026517715","https://openalex.org/W2039608780","https://openalex.org/W2042408356","https://openalex.org/W2081754635","https://openalex.org/W2094075263","https://openalex.org/W2099712288","https://openalex.org/W2108111657","https://openalex.org/W2133255058","https://openalex.org/W2140738666","https://openalex.org/W2149189207","https://openalex.org/W2165114467","https://openalex.org/W2166774562","https://openalex.org/W3101550650","https://openalex.org/W6785489703"],"related_works":["https://openalex.org/W1972096828","https://openalex.org/W2529137940","https://openalex.org/W4302048708","https://openalex.org/W2359913921","https://openalex.org/W1595194509","https://openalex.org/W4239740410","https://openalex.org/W1996195943","https://openalex.org/W4205298958","https://openalex.org/W2486440955","https://openalex.org/W2323190011"],"abstract_inverted_index":{"Reconstructing":[0],"depth":[1,38,55,110,141,145],"map":[2],"from":[3,15,56],"a":[4,53],"sequence":[5],"of":[6,109,116],"images":[7],"with":[8],"different":[9],"camera":[10],"settings,":[11],"known":[12,98],"as":[13,99],"Depth":[14],"Focus":[16],"(DFF),":[17],"has":[18],"been":[19],"addressed":[20],"for":[21],"decades.":[22],"However,":[23],"Classical":[24],"local":[25,89],"methods,":[26],"e.g.,":[27],"windowed":[28],"filter,":[29],"usually":[30],"produce":[31],"uncertain":[32],"estimation":[33],"in":[34],"textureless":[35],"areas":[36],"and":[37,47,64,92,123,139],"discontinuities.":[39,146],"Meanwhile,":[40],"they":[41],"appear":[42],"sensitive":[43],"to":[44,68,86,104],"filter":[45],"size":[46],"image":[48],"noise.":[49],"In":[50],"this":[51,117],"research":[52],"semi-global":[54],"focus":[57,90],"approach":[58,135],"that":[59,132],"considers":[60],"both":[61,121],"the":[62,88,106,114,133],"accuracy":[63],"robustness":[65],"is":[66,84,102],"proposed":[67,85,134],"enforce":[69],"an":[70,80,94],"adaptive":[71,81],"smoothness":[72],"constraint":[73],"based":[74],"on":[75,120],"modified":[76],"Laplacian":[77],"measure.":[78],"Firstly":[79],"aggregation":[82],"strategy":[83],"integrate":[87],"measure":[91],"then":[93],"efficient":[95],"optimization":[96],"method,":[97],"scanline":[100],"optimization,":[101],"introduced":[103],"approximate":[105],"global":[107],"optimal":[108],"estimation.":[111],"To":[112],"evaluate":[113],"effectiveness":[115],"research,":[118],"experiments":[119],"synthetic":[122],"real":[124],"datasets":[125],"are":[126],"performed.":[127],"The":[128],"experimental":[129],"results":[130],"show":[131],"generates":[136],"more":[137],"robust":[138],"accurate":[140],"maps":[142],"while":[143],"preserving":[144]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}