{"id":"https://openalex.org/W1932556813","doi":"https://doi.org/10.5220/0004667402700279","title":"Likelihood Functions for Errors-in-variables Models - Bias-free Local Estimation with Minimum Variance","display_name":"Likelihood Functions for Errors-in-variables Models - Bias-free Local Estimation with Minimum Variance","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W1932556813","doi":"https://doi.org/10.5220/0004667402700279","mag":"1932556813"},"language":"en","primary_location":{"id":"doi:10.5220/0004667402700279","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0004667402700279","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Computer Vision Theory and Applications","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0004667402700279","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016852627","display_name":"Kai Krajsek","orcid":"https://orcid.org/0000-0003-3417-161X"},"institutions":[{"id":"https://openalex.org/I171892758","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I171892758"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Kai Krajsek","raw_affiliation_strings":["IBG-2: Plant Sciences, Forschungszentrum J\u00fclich, 52425, Germany"],"affiliations":[{"raw_affiliation_string":"IBG-2: Plant Sciences, Forschungszentrum J\u00fclich, 52425, Germany","institution_ids":["https://openalex.org/I171892758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089950477","display_name":"Christian Heinemann","orcid":"https://orcid.org/0000-0003-0734-700X"},"institutions":[{"id":"https://openalex.org/I171892758","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I171892758"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Heinemann","raw_affiliation_strings":["IBG-2: Plant Sciences, Forschungszentrum J\u00fclich, 52425, Germany"],"affiliations":[{"raw_affiliation_string":"IBG-2: Plant Sciences, Forschungszentrum J\u00fclich, 52425, Germany","institution_ids":["https://openalex.org/I171892758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058673315","display_name":"Hanno Scharr","orcid":"https://orcid.org/0000-0002-8555-6416"},"institutions":[{"id":"https://openalex.org/I171892758","display_name":"Forschungszentrum J\u00fclich","ror":"https://ror.org/02nv7yv05","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I171892758"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hanno Scharr","raw_affiliation_strings":["IBG-2: Plant Sciences, Forschungszentrum J\u00fclich, 52425, Germany"],"affiliations":[{"raw_affiliation_string":"IBG-2: Plant Sciences, Forschungszentrum J\u00fclich, 52425, Germany","institution_ids":["https://openalex.org/I171892758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016852627"],"corresponding_institution_ids":["https://openalex.org/I171892758"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04277663,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"270","last_page":"279"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9929999709129333,"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/variance","display_name":"Variance (accounting)","score":0.6994431018829346},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6215724945068359},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5984464883804321},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.4563186764717102},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.45616039633750916},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45381802320480347},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4113110899925232},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3795461356639862},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.14079955220222473},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11451900005340576},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.07778993248939514}],"concepts":[{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6994431018829346},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6215724945068359},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5984464883804321},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.4563186764717102},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.45616039633750916},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45381802320480347},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4113110899925232},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3795461356639862},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.14079955220222473},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11451900005340576},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.07778993248939514},{"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/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5220/0004667402700279","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0004667402700279","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Computer Vision Theory and Applications","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5220/0004667402700279","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0004667402700279","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Computer Vision Theory and Applications","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W654294952","https://openalex.org/W1495111467","https://openalex.org/W1509069826","https://openalex.org/W1542808181","https://openalex.org/W1571601685","https://openalex.org/W2015881065","https://openalex.org/W2029088553","https://openalex.org/W2033959528","https://openalex.org/W2034869517","https://openalex.org/W2043225268","https://openalex.org/W2053610073","https://openalex.org/W2071327759","https://openalex.org/W2083786269","https://openalex.org/W2106062873","https://openalex.org/W2114449024","https://openalex.org/W2118877769","https://openalex.org/W2143476164","https://openalex.org/W2143664173","https://openalex.org/W2147245178","https://openalex.org/W2152976633","https://openalex.org/W2158075041","https://openalex.org/W3003662786","https://openalex.org/W3020339995"],"related_works":["https://openalex.org/W2349547417","https://openalex.org/W2910434125","https://openalex.org/W2002793373","https://openalex.org/W2463762105","https://openalex.org/W2166923183","https://openalex.org/W2005426889","https://openalex.org/W2380964641","https://openalex.org/W2394058734","https://openalex.org/W2244484926","https://openalex.org/W2030559341"],"abstract_inverted_index":{"Parameter":[0],"estimation":[1],"in":[2,55,139],"the":[3,56,59,116,152,178],"presence":[4],"of":[5,12,18,58,81,98,115,180],"noisy":[6],"measurements":[7],"characterizes":[8],"a":[9,32,50,69,74,94,99,104],"wide":[10],"range":[11],"computer":[13],"vision":[14],"problems.":[15,26],"Thus,":[16],"many":[17],"them":[19],"can":[20],"be":[21],"formulated":[22],"as":[23,108,110,132],"errors-in-variables":[24],"(EIV)":[25],"In":[27],"this":[28],"paper":[29],"we":[30,72,85],"provide":[31,73,86],"closed":[33,51],"form":[34],"likelihood":[35,70,89],"function":[36],"to":[37,147,160],"EIV":[38,82,92],"problems":[39],"with":[40,158],"arbitrary":[41],"covariance":[42,60],"structure.":[43],"Previous":[44],"approaches":[45],"either":[46],"do":[47],"not":[48],"offer":[49],"form,":[52],"are":[53],"restricted":[54],"structure":[57],"matrix,":[61],"or":[62],"involve":[63],"nuisance":[64],"parameters.":[65],"By":[66],"using":[67],"such":[68],"function,":[71],"theoretical":[75],"justification":[76],"for":[77,91],"well":[78,100,109,135],"established":[79],"estimators":[80,90],"models.":[83],"Furthermore":[84],"two":[87],"maximum":[88],"parameters,":[93],"straight":[95,142],"forward":[96,143],"extension":[97,144],"known":[101],"estimator":[102],"and":[103,137],"novel,":[105,153],"local":[106,154],"estimator,":[107],"confidence":[111],"bounds":[112],"by":[113,125],"means":[114],"Cramer":[117],"Rao":[118],"Lower":[119],"Bound.":[120],"We":[121],"show":[122],"their":[123],"performance":[124],"numerical":[126],"experiments":[127],"on":[128,177],"optical":[129,174],"flow":[130,175],"estimation,":[131],"it":[133,166],"is":[134],"explored":[136],"understood":[138],"literature.":[140],"The":[141],"turned":[145],"out":[146],"have":[148],"oscillating":[149],"behavior,":[150],"while":[151],"one":[155],"performs":[156,168],"favorably":[157],"respect":[159],"other":[161],"methods.":[162],"For":[163],"small":[164],"motions,":[165],"even":[167],"better":[169],"than":[170],"an":[171],"excellent":[172],"global":[173],"algorithm":[176],"majority":[179],"pixel":[181],"locations.":[182]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2016-06-24T00:00:00"}