{"id":"https://openalex.org/W2568437493","doi":"https://doi.org/10.1109/iecon.2016.7793888","title":"Tessellation for Wide Angle Foveated image with 4 regions based on overlapping circular receptive field mapping","display_name":"Tessellation for Wide Angle Foveated image with 4 regions based on overlapping circular receptive field mapping","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2568437493","doi":"https://doi.org/10.1109/iecon.2016.7793888","mag":"2568437493"},"language":"en","primary_location":{"id":"doi:10.1109/iecon.2016.7793888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2016.7793888","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},"type":"conference-paper","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/A5112678712","display_name":"Sota Shimizu","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sota Shimizu","raw_affiliation_strings":["Keio University, Graduate School of Science and Technology, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University, Graduate School of Science and Technology, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111408646","display_name":"N. Hasebe","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nobuyuki Hasebe","raw_affiliation_strings":["Waseda University, Research Institute for Science and Engineering, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University, Research Institute for Science and Engineering, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6187","last_page":"6192"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10531","display_name":"Advanced Vision and Imaging","score":0.9980000257492065,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7875940799713135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6855690479278564},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6593787670135498},{"id":"https://openalex.org/keywords/field-of-view","display_name":"Field of view","score":0.645213782787323},{"id":"https://openalex.org/keywords/human-eye","display_name":"Human eye","score":0.6043245792388916},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5483664870262146},{"id":"https://openalex.org/keywords/visual-angle","display_name":"Visual angle","score":0.534841001033783},{"id":"https://openalex.org/keywords/peripheral-vision","display_name":"Peripheral vision","score":0.5163872838020325},{"id":"https://openalex.org/keywords/tessellation","display_name":"Tessellation (computer graphics)","score":0.462529718875885},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.45174312591552734},{"id":"https://openalex.org/keywords/image-sensor","display_name":"Image sensor","score":0.41718894243240356},{"id":"https://openalex.org/keywords/human-visual-system-model","display_name":"Human visual system model","score":0.41577354073524475},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3732525110244751},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3248125910758972},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12071391940116882}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7875940799713135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6855690479278564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6593787670135498},{"id":"https://openalex.org/C150627866","wikidata":"https://www.wikidata.org/wiki/Q1076893","display_name":"Field of view","level":2,"score":0.645213782787323},{"id":"https://openalex.org/C2781427961","wikidata":"https://www.wikidata.org/wiki/Q430024","display_name":"Human eye","level":2,"score":0.6043245792388916},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5483664870262146},{"id":"https://openalex.org/C200756006","wikidata":"https://www.wikidata.org/wiki/Q336092","display_name":"Visual angle","level":2,"score":0.534841001033783},{"id":"https://openalex.org/C185761835","wikidata":"https://www.wikidata.org/wiki/Q1431287","display_name":"Peripheral vision","level":2,"score":0.5163872838020325},{"id":"https://openalex.org/C43817857","wikidata":"https://www.wikidata.org/wiki/Q9357547","display_name":"Tessellation (computer graphics)","level":2,"score":0.462529718875885},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.45174312591552734},{"id":"https://openalex.org/C76935873","wikidata":"https://www.wikidata.org/wiki/Q209121","display_name":"Image sensor","level":2,"score":0.41718894243240356},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.41577354073524475},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3732525110244751},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3248125910758972},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12071391940116882},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon.2016.7793888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2016.7793888","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3914349193","display_name":"Development of Liquid Crstal Wide Angle Fovea Sensor by which Attention Point can Change Rapidly Without Any Mechanical Part","funder_award_id":"15K05914","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320322638","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1506110186","https://openalex.org/W1509866307","https://openalex.org/W1585967164","https://openalex.org/W1937677733","https://openalex.org/W1938248414","https://openalex.org/W1994734056","https://openalex.org/W2007899294","https://openalex.org/W2011937936","https://openalex.org/W2051595870","https://openalex.org/W2157107370","https://openalex.org/W2330624871","https://openalex.org/W2512686442","https://openalex.org/W2554828352"],"related_works":["https://openalex.org/W2367741774","https://openalex.org/W2369815077","https://openalex.org/W4308065813","https://openalex.org/W2323521801","https://openalex.org/W2271062915","https://openalex.org/W1982147132","https://openalex.org/W4309735723","https://openalex.org/W2429008829","https://openalex.org/W4321369532","https://openalex.org/W2586696303"],"abstract_inverted_index":{"As":[0],"well-known,":[1],"the":[2,43,58,69,100,113,133,161],"human":[3,44,70,102,134],"single":[4],"eye":[5,71,92],"has":[6,22],"a":[7,41,73,123,146,157,172],"horizontally":[8],"approximately":[9],"120-degree":[10],"wide":[11,64],"FOV.":[12,65],"Its":[13],"FOV":[14,21,31,53],"is":[15,32,111,129,145,164,177],"wide,":[16],"but":[17],"only":[18],"its":[19,29,37,51,63],"central":[20,52],"high":[23],"visual":[24,81,135],"acuity.":[25,136],"Visual":[26],"acuity":[27],"in":[28,48,156],"peripheral":[30],"very":[33],"low.":[34],"By":[35],"changing":[36],"view":[38],"line":[39],"into":[40,98],"target,":[42],"can":[45,56,139],"observe":[46,57],"it":[47],"detail":[49],"by":[50,62,89],"and":[54,154,179],"simultaneously":[55],"whole":[59],"of":[60,109,112],"environment":[61,88],"This":[66,144],"means":[67],"that":[68],"performs":[72],"smart":[74],"data":[75,85,142,151],"acquisition,":[76],"i.e.,":[77,118,181],"getting":[78],"more":[79],"detailed":[80],"information":[82],"with":[83,188],"smaller":[84],"amount":[86],"from":[87],"combining":[90],"appropriate":[91],"movements.":[93],"WAFVS":[94,110,138,176],"was":[95],"investigated":[96],"taking":[97],"account":[99],"above":[101],"eye's":[103],"functionality.":[104],"The":[105],"most":[106],"characterized":[107],"point":[108],"resolution-variant":[114],"input":[115],"image":[116,125,141,150,187],"data,":[117],"this":[119,170],"vision":[120,162],"sensor":[121,163],"gets":[122],"wide-angle":[124],"where":[126],"spatial":[127],"resolution":[128],"not":[130],"uniform":[131],"like":[132],"Therefore,":[137],"reduce":[140],"hugely.":[143],"strong":[147],"advantage":[148],"when":[149,160],"are":[152],"transmitted":[153],"recorded":[155],"storage":[158],"device":[159],"applied":[165],"for":[166,175,183],"remote":[167],"operation.":[168],"In":[169],"paper,":[171],"remapping":[173],"method":[174],"proposed":[178],"implemented,":[180],"tessellation":[182],"Wide":[184],"Angle":[185],"Foveated":[186],"4":[189],"regions":[190],"based":[191],"on":[192],"overlapping":[193],"circular":[194],"RF":[195],"mapping.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}