{"id":"https://openalex.org/W2789867180","doi":"https://doi.org/10.1109/vcip.2017.8305146","title":"Two-stream binocular network: Accurate near field finger detection based on binocular images","display_name":"Two-stream binocular network: Accurate near field finger detection based on binocular images","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2789867180","doi":"https://doi.org/10.1109/vcip.2017.8305146","mag":"2789867180"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2017.8305146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1804.10160","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100339158","display_name":"Yi Wei","orcid":"https://orcid.org/0000-0002-9886-6851"},"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":"Yi Wei","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045183950","display_name":"Guijin Wang","orcid":"https://orcid.org/0000-0002-2131-3044"},"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":"Guijin Wang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108046736","display_name":"Cai\u2010Rong Zhang","orcid":"https://orcid.org/0000-0002-4067-2798"},"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":"Cairong Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100542891","display_name":"Hengkai Guo","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":"Hengkai Guo","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006817088","display_name":"Xinghao Chen","orcid":"https://orcid.org/0000-0002-2102-8235"},"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":"Xinghao Chen","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023755254","display_name":"Huazhong Yang","orcid":"https://orcid.org/0000-0003-2421-353X"},"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":"Huazhong Yang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100339158"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.22536323,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57058317,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"33","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9995999932289124,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9900000095367432,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9883000254631042,"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/computer-science","display_name":"Computer science","score":0.7739244699478149},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7640856504440308},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.701586127281189},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5725374221801758},{"id":"https://openalex.org/keywords/binocular-vision","display_name":"Binocular vision","score":0.5690995454788208},{"id":"https://openalex.org/keywords/binocular-disparity","display_name":"Binocular disparity","score":0.49330344796180725},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44949978590011597},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.44884076714515686},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41738513112068176},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4173475503921509},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4034116864204407}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7739244699478149},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7640856504440308},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.701586127281189},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5725374221801758},{"id":"https://openalex.org/C121958486","wikidata":"https://www.wikidata.org/wiki/Q609543","display_name":"Binocular vision","level":2,"score":0.5690995454788208},{"id":"https://openalex.org/C90790637","wikidata":"https://www.wikidata.org/wiki/Q11681118","display_name":"Binocular disparity","level":3,"score":0.49330344796180725},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44949978590011597},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.44884076714515686},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41738513112068176},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4173475503921509},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4034116864204407},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/vcip.2017.8305146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1804.10160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1804.10160","pdf_url":"https://arxiv.org/pdf/1804.10160","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1804.10160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1804.10160","pdf_url":"https://arxiv.org/pdf/1804.10160","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1702419847","https://openalex.org/W1928739709","https://openalex.org/W1990947293","https://openalex.org/W2075156252","https://openalex.org/W2076139799","https://openalex.org/W2194775991","https://openalex.org/W2541674938","https://openalex.org/W2546353648","https://openalex.org/W2572423989","https://openalex.org/W2599765304","https://openalex.org/W2950094539","https://openalex.org/W2963323939","https://openalex.org/W2963410064","https://openalex.org/W3105217837","https://openalex.org/W6620707391","https://openalex.org/W6637606113"],"related_works":["https://openalex.org/W2168424431","https://openalex.org/W2026531732","https://openalex.org/W994764823","https://openalex.org/W2016611802","https://openalex.org/W4236907531","https://openalex.org/W2004704930","https://openalex.org/W2170303590","https://openalex.org/W2037634797","https://openalex.org/W4238197581","https://openalex.org/W100859609"],"abstract_inverted_index":{"Fingertip":[0],"detection":[1],"plays":[2],"an":[3,125],"important":[4],"role":[5],"in":[6,73,111,119],"human":[7],"computer":[8],"interaction.":[9],"Previous":[10],"works":[11],"transform":[12],"binocular":[13,50,84,101],"images":[14,51,110,118],"into":[15],"depth":[16],"images.":[17,66],"Then":[18,67],"depth-based":[19],"hand":[20,102],"pose":[21],"estimation":[22],"methods":[23,123],"are":[24],"used":[25],"to":[26,46,88],"predict":[27],"3D":[28],"positions":[29],"of":[30,62,91,109,117,128],"fingertips.":[31],"Different":[32],"from":[33,49],"previous":[34,135],"works,":[35],"we":[36,79,98],"propose":[37],"a":[38,81,100],"new":[39,82],"framework,":[40],"named":[41],"Two-Stream":[42],"Binocular":[43],"Network":[44],"(TSBnet)":[45],"detect":[47],"fingertips":[48],"directly.":[52],"TSBnet":[53],"first":[54],"shares":[55],"convolutional":[56,75],"layers":[57],"for":[58],"low":[59],"level":[60,71],"features":[61,72],"right":[63],"and":[64,114],"left":[65],"it":[68],"extracts":[69],"high":[70],"two-stream":[74],"networks":[76],"separately.":[77],"Further,":[78],"add":[80],"layer:":[83],"distance":[85],"measurement":[86],"layer":[87],"improve":[89],"performance":[90],"our":[92,96,131],"model.":[93],"To":[94],"verify":[95],"scheme,":[97],"build":[99],"image":[103],"dataset,":[104],"containing":[105],"about":[106],"117k":[107],"pairs":[108,116],"training":[112],"set":[113],"10k":[115],"test":[120,132],"set.":[121],"Our":[122],"achieve":[124],"average":[126],"error":[127],"10.9mm":[129],"on":[130],"set,":[133],"outperforming":[134],"work":[136],"by":[137],"5.9mm":[138],"(relatively":[139],"35.1%).":[140]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2018-03-29T00:00:00"}
