{"id":"https://openalex.org/W2903522630","doi":"https://doi.org/10.1109/icpr.2018.8546330","title":"Dynamic Projected Segmentation Networks For Hand Pose Estimation","display_name":"Dynamic Projected Segmentation Networks For Hand Pose Estimation","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903522630","doi":"https://doi.org/10.1109/icpr.2018.8546330","mag":"2903522630"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8546330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5110814371","display_name":"Yunlong Che","orcid":"https://orcid.org/0000-0003-1203-3240"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunlong Che","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101491985","display_name":"Yue Qi","orcid":"https://orcid.org/0000-0001-9304-1933"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Qi","raw_affiliation_strings":["Qingdao Research Institute of Beihang University, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Qingdao Research Institute of Beihang University, Qingdao, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110814371"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.5433,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.68289071,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"33","issue":null,"first_page":"477","last_page":"482"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":1.0,"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":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","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/T10653","display_name":"Robot Manipulation and Learning","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/segmentation","display_name":"Segmentation","score":0.8241003751754761},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7743253111839294},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.772219181060791},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.7277083396911621},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5781707167625427},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5291058421134949},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.48421144485473633},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.48291561007499695},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4465307891368866},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.440823495388031},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3936021625995636}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8241003751754761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7743253111839294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.772219181060791},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7277083396911621},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5781707167625427},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5291058421134949},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.48421144485473633},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.48291561007499695},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4465307891368866},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.440823495388031},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3936021625995636},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8546330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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":26,"referenced_works":["https://openalex.org/W1442440875","https://openalex.org/W1480138144","https://openalex.org/W1928739709","https://openalex.org/W1952857803","https://openalex.org/W1974700302","https://openalex.org/W1990947293","https://openalex.org/W2007104354","https://openalex.org/W2023633446","https://openalex.org/W2075156252","https://openalex.org/W2084455417","https://openalex.org/W2093414253","https://openalex.org/W2100642335","https://openalex.org/W2113325037","https://openalex.org/W2134538062","https://openalex.org/W2137940226","https://openalex.org/W2155893237","https://openalex.org/W2172156083","https://openalex.org/W2210697964","https://openalex.org/W2469784314","https://openalex.org/W2950094539","https://openalex.org/W2963613382","https://openalex.org/W2963881378","https://openalex.org/W3148721422","https://openalex.org/W6628340477","https://openalex.org/W6676698716","https://openalex.org/W6719699370"],"related_works":["https://openalex.org/W4253893311","https://openalex.org/W2798721181","https://openalex.org/W3201205132","https://openalex.org/W4287600488","https://openalex.org/W4312694060","https://openalex.org/W4386075737","https://openalex.org/W4281696776","https://openalex.org/W4387967917","https://openalex.org/W4299867837","https://openalex.org/W2951583186"],"abstract_inverted_index":{"Hand":[0],"pose":[1,23,39,97,108],"estimation":[2,24],"in":[3],"depth":[4,52,81],"images":[5],"is":[6,86],"a":[7,18,55,68,90],"challenging":[8,131],"problem":[9],"for":[10,21,94],"human-computer":[11],"interaction.":[12],"In":[13],"this":[14],"paper,":[15],"we":[16,44,66,99],"propose":[17,45],"novel":[19],"approach":[20,125],"hand":[22,34,42,78,118],"that":[25,123],"shares":[26],"the":[27,61,77,106,111],"merits":[28],"of":[29],"both":[30],"deep":[31],"learning":[32],"based":[33,38,71,109],"segmentation":[35,112],"and":[36],"dynamics":[37,103],"optimization.":[40],"For":[41,96],"segmentation,":[43],"`Dynamic":[46],"Projected":[47],"Segmentation":[48],"Networks'":[49,93],"applied":[50],"at":[51],"images,":[53],"providing":[54],"pixel-wise":[56],"classification":[57],"result.":[58],"To":[59],"preserve":[60],"detailed":[62],"hand-region":[63,72,85],"topology":[64],"structure,":[65],"design":[67],"dynamic":[69],"projection":[70],"extraction":[73],"method":[74],"to":[75,104],"crop":[76],"region":[79],"from":[80],"images.":[82],"The":[83],"projected":[84],"then":[87],"fed":[88],"into":[89],"light-weight":[91],"`Encoder-Decoder":[92],"segmentation.":[95],"optimization,":[98],"employ":[100],"rigid":[101],"body":[102],"estimate":[105],"final":[107],"on":[110,129],"results":[113],"which":[114],"are":[115],"treated":[116],"as":[117],"geometry":[119],"constraints.":[120],"Experiments":[121],"show":[122],"our":[124],"outperforms":[126],"state-of-the-art":[127],"methods":[128],"two":[130],"datasets.":[132]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
