{"id":"https://openalex.org/W2564420833","doi":"https://doi.org/10.1109/icip.2016.7532869","title":"Mouse calibration aided real-time gaze estimation based on boost Gaussian Bayesian learning","display_name":"Mouse calibration aided real-time gaze estimation based on boost Gaussian Bayesian learning","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2564420833","doi":"https://doi.org/10.1109/icip.2016.7532869","mag":"2564420833"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532869","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532869","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/A5077493772","display_name":"Nanyang Ye","orcid":"https://orcid.org/0000-0003-3129-3953"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Nanyang Ye","raw_affiliation_strings":["Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, P. R. China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101459083","display_name":"Xiaoming Tao","orcid":"https://orcid.org/0000-0003-2168-0044"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Tao","raw_affiliation_strings":["Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, P. R. China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000236213","display_name":"Linhao Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linhao Dong","raw_affiliation_strings":["Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, P. R. China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053547427","display_name":"Ning Ge","orcid":"https://orcid.org/0000-0002-4922-7025"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Ge","raw_affiliation_strings":["Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, P. R. China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5077493772"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.5398,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70519762,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"114","issue":null,"first_page":"2797","last_page":"2801"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.9660000205039978,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10250","display_name":"Glaucoma and retinal disorders","score":0.9473000168800354,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"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":0.8288999795913696},{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.7566393613815308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6373467445373535},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6373319625854492},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5159233808517456},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5065642595291138},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.4592478275299072},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4515116810798645},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.354910671710968},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3448212742805481},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09411296248435974}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8288999795913696},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.7566393613815308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6373467445373535},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6373319625854492},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5159233808517456},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5065642595291138},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.4592478275299072},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4515116810798645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.354910671710968},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3448212742805481},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09411296248435974},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532869","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532869","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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":23,"referenced_works":["https://openalex.org/W91494418","https://openalex.org/W1643185111","https://openalex.org/W1995694455","https://openalex.org/W2034727963","https://openalex.org/W2046604392","https://openalex.org/W2048968870","https://openalex.org/W2049807536","https://openalex.org/W2059916030","https://openalex.org/W2087862817","https://openalex.org/W2096294213","https://openalex.org/W2100548715","https://openalex.org/W2116087590","https://openalex.org/W2149382413","https://openalex.org/W2157285372","https://openalex.org/W2164598857","https://openalex.org/W2167020116","https://openalex.org/W2183596224","https://openalex.org/W2397023066","https://openalex.org/W3092021191","https://openalex.org/W4230946174","https://openalex.org/W4285719527","https://openalex.org/W6711844864","https://openalex.org/W6784024923"],"related_works":["https://openalex.org/W1880689012","https://openalex.org/W3014378845","https://openalex.org/W4240909707","https://openalex.org/W2059546927","https://openalex.org/W3207760378","https://openalex.org/W4386072035","https://openalex.org/W2562758970","https://openalex.org/W1986970529","https://openalex.org/W2161294397","https://openalex.org/W2012644758"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,64,84],"propose":[4],"a":[5,21,48,69],"novel":[6],"gaze":[7,57,107],"estimation":[8,108],"method":[9,25,31,99,103],"to":[10,46,56,89],"evaluate":[11],"the":[12,52,92,101],"attention":[13],"span":[14],"of":[15,35],"users":[16,78],"upon":[17],"on-screen":[18],"content":[19],"via":[20],"single":[22],"webcam.":[23],"Our":[24],"is":[26],"based":[27],"on":[28],"supervised":[29],"descent":[30],"for":[32,74,110],"eye":[33,54],"region":[34],"interest":[36],"(ROI)":[37],"extraction.":[38],"Then,":[39],"boost":[40],"Gaussian":[41],"Bayesian":[42],"regressors":[43],"are":[44],"applied":[45],"learn":[47],"robust":[49],"mapping":[50],"from":[51,77],"input":[53],"ROI":[55],"coordinates.":[58],"To":[59,81],"get":[60],"enough":[61],"training":[62],"samples,":[63],"implant":[65],"our":[66,98],"scheme":[67],"as":[68],"plug-in":[70],"into":[71],"web":[72],"browsers":[73],"data":[75,109],"collection":[76],"without":[79],"bothering.":[80],"improve":[82],"accuracy,":[83],"also":[85],"introduce":[86],"mouse":[87],"click":[88],"help":[90],"train":[91],"regressors.":[93],"Experiment":[94],"results":[95],"show":[96],"that":[97],"outperforms":[100],"existing":[102],"and":[104],"can":[105],"provide":[106],"user":[111],"behaviour":[112],"analysis":[113],"in":[114],"real-time":[115],"implementation.":[116]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}