{"id":"https://openalex.org/W4376121640","doi":"https://doi.org/10.1109/tvcg.2023.3274595","title":"Hypothesis Testing for Progressive Kernel Estimation and VCM Framework","display_name":"Hypothesis Testing for Progressive Kernel Estimation and VCM Framework","publication_year":2023,"publication_date":"2023-05-10","ids":{"openalex":"https://openalex.org/W4376121640","doi":"https://doi.org/10.1109/tvcg.2023.3274595","pmid":"https://pubmed.ncbi.nlm.nih.gov/37159325"},"language":"en","primary_location":{"id":"doi:10.1109/tvcg.2023.3274595","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tvcg.2023.3274595","pdf_url":null,"source":{"id":"https://openalex.org/S84775595","display_name":"IEEE Transactions on Visualization and Computer Graphics","issn_l":"1077-2626","issn":["1077-2626","1941-0506","2160-9306"],"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 Visualization and Computer Graphics","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2504.04411","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112585988","display_name":"Zehui Lin","orcid":"https://orcid.org/0009-0008-7855-4296"},"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":"Zehui Lin","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-7855-4296","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111226450","display_name":"Chenxiao Hu","orcid":"https://orcid.org/0009-0000-3306-6653"},"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":"Chenxiao Hu","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-3306-6653","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052472773","display_name":"Jinzhu Jia","orcid":"https://orcid.org/0000-0002-6554-2463"},"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":"Jinzhu Jia","raw_affiliation_strings":["Department of Biostatistics and Center for Statistical Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6554-2463","affiliations":[{"raw_affiliation_string":"Department of Biostatistics and Center for Statistical Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100359825","display_name":"Sheng Li","orcid":"https://orcid.org/0000-0002-8901-2184"},"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":"Sheng Li","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8901-2184","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.1599,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5003046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"30","issue":"8","first_page":"4709","last_page":"4723"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9854999780654907,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7402812242507935},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5714861154556274},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.472277969121933},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.41168057918548584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3990585207939148},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37765341997146606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33666855096817017},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2751704454421997},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16197654604911804},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09870919585227966}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7402812242507935},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5714861154556274},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.472277969121933},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.41168057918548584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3990585207939148},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37765341997146606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33666855096817017},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2751704454421997},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16197654604911804},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09870919585227966},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tvcg.2023.3274595","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tvcg.2023.3274595","pdf_url":null,"source":{"id":"https://openalex.org/S84775595","display_name":"IEEE Transactions on Visualization and Computer Graphics","issn_l":"1077-2626","issn":["1077-2626","1941-0506","2160-9306"],"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 Visualization and Computer Graphics","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2504.04411","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.04411","pdf_url":"https://arxiv.org/pdf/2504.04411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"},{"id":"pmid:37159325","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37159325","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on visualization and computer graphics","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2504.04411","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.04411","pdf_url":"https://arxiv.org/pdf/2504.04411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7046862976","display_name":null,"funder_award_id":"2022ZD0160805","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W404240460","https://openalex.org/W1797096580","https://openalex.org/W1982871508","https://openalex.org/W1986743298","https://openalex.org/W1986878934","https://openalex.org/W1990159348","https://openalex.org/W2008870867","https://openalex.org/W2018913979","https://openalex.org/W2027979483","https://openalex.org/W2033962550","https://openalex.org/W2040093013","https://openalex.org/W2041282815","https://openalex.org/W2058810187","https://openalex.org/W2064193045","https://openalex.org/W2094199382","https://openalex.org/W2104814486","https://openalex.org/W2142380402","https://openalex.org/W2151047098","https://openalex.org/W2152859945","https://openalex.org/W2153935912","https://openalex.org/W2331600205","https://openalex.org/W2556575547","https://openalex.org/W2731574721","https://openalex.org/W2788256404","https://openalex.org/W2956353133","https://openalex.org/W2979652999","https://openalex.org/W3042547367","https://openalex.org/W3106552726","https://openalex.org/W3108103428","https://openalex.org/W3169556391","https://openalex.org/W4229879999","https://openalex.org/W4238193180","https://openalex.org/W4240259818","https://openalex.org/W4243367687","https://openalex.org/W4245933748","https://openalex.org/W4286611184","https://openalex.org/W6607745231","https://openalex.org/W6613934775","https://openalex.org/W6635167953","https://openalex.org/W6748504987"],"related_works":["https://openalex.org/W2576994247","https://openalex.org/W2608353378","https://openalex.org/W4249206767","https://openalex.org/W2563559453","https://openalex.org/W2382330008","https://openalex.org/W1519970947","https://openalex.org/W4243860260","https://openalex.org/W4383503847","https://openalex.org/W2961085424","https://openalex.org/W2342695897"],"abstract_inverted_index":{"Identifying":[0],"an":[1,205],"appropriate":[2],"radius":[3,20,102,149],"for":[4,10,42,109],"unbiased":[5,52,110,129],"kernel":[6,44,49,101,148],"estimation":[7,50],"is":[8,51,103],"crucial":[9],"the":[11,19,48,54,72,76,83,86,100,152,197,209],"efficiency":[12],"of":[13,36,57,88,119,189,200],"radiance":[14,111,191],"estimation.":[15,112],"However,":[16],"determining":[17],"both":[18],"and":[21,39,122,125,156,165,185,203],"unbiasedness":[22],"still":[23],"faces":[24],"big":[25],"challenges.":[26],"In":[27],"this":[28,58,106],"paper,":[29],"we":[30,63,91,114],"first":[31],"propose":[32,115],"a":[33,65,93,117],"statistical":[34,59,77],"model":[35,60],"photon":[37,80,95],"samples":[38],"associated":[40],"contributions":[41,153],"progressive":[43,94],"estimation,":[45],"under":[46],"which":[47],"if":[53],"null":[55,73],"hypothesis":[56,74,107,133],"stands.":[61],"Then,":[62],"present":[64],"method":[66,180],"to":[67,70],"decide":[68],"whether":[69],"reject":[71],"about":[75],"population":[78],"(i.e.,":[79],"samples)":[81],"by":[82,105],"F-test":[84],"in":[85,211],"Analysis":[87],"Variance.":[89],"Hereby,":[90],"implement":[92],"mapping":[96],"(PPM)":[97],"algorithm,":[98],"wherein":[99,146],"determined":[104],"test":[108,159],"Second,":[113],"VCM+,":[116,166],"reinforcement":[118],"Vertex":[120],"Connection":[121],"Merging":[123],"(VCM),":[124],"derive":[126],"its":[127],"theoretically":[128],"formulation.":[130],"VCM+":[131],"combines":[132],"testing-based":[134],"PPM":[135,155,164],"with":[136,170],"bidirectional":[137],"path":[138],"tracing":[139],"(BDPT)":[140],"via":[141],"multiple":[142],"importance":[143],"sampling":[144],"(MIS),":[145],"our":[147,160,179,201],"can":[150,181],"leverage":[151],"from":[154],"BDPT.":[157],"We":[158,194],"new":[161],"algorithms,":[162],"improved":[163],"on":[167],"diverse":[168],"scenarios":[169],"different":[171],"lighting":[172],"settings.":[173],"The":[174],"experimental":[175],"results":[176],"demonstrate":[177],"that":[178],"alleviate":[182],"light":[183],"leaks":[184],"visual":[186],"blur":[187],"artifacts":[188],"prior":[190],"estimate":[192],"algorithms.":[193],"also":[195],"evaluate":[196],"asymptotic":[198],"performance":[199],"approach":[202],"observe":[204],"overall":[206],"improvement":[207],"over":[208],"baseline":[210],"all":[212],"testing":[213],"scenarios.":[214]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}