{"id":"https://openalex.org/W6907550151","doi":"https://doi.org/10.2312/sca.20141135","title":"Efficient Unsupervised Temporal Segmentation of Human Motion","display_name":"Efficient Unsupervised Temporal Segmentation of Human Motion","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W6907550151","doi":"https://doi.org/10.2312/sca.20141135"},"language":"en","primary_location":{"id":"doi:10.2312/sca.20141135","is_oa":true,"landing_page_url":"https://doi.org/10.2312/sca.20141135","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/sca.20141135","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"V\u00f6gele, Anna","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"V\u00f6gele, Anna","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Kr\u00fcger, Bj\u00f6rn","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kr\u00fcger, Bj\u00f6rn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Klein, Reinhard","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klein, Reinhard","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6499000191688538},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5871000289916992},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5248000025749207},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47920000553131104},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.4778999984264374},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.41519999504089355},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4122999906539917},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.40119999647140503},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.39969998598098755}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7009999752044678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6938999891281128},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6499000191688538},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5871000289916992},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5396000146865845},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5248000025749207},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47920000553131104},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.4778999984264374},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4122999906539917},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.39969998598098755},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.39089998602867126},{"id":"https://openalex.org/C2986578859","wikidata":"https://www.wikidata.org/wiki/Q657632","display_name":"Human motion","level":3,"score":0.3873000144958496},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.34389999508857727},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C2777036941","wikidata":"https://www.wikidata.org/wiki/Q6917771","display_name":"Motion analysis","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3116999864578247},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C146159030","wikidata":"https://www.wikidata.org/wiki/Q7625099","display_name":"Structure from motion","level":3,"score":0.2992999851703644},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C182124507","wikidata":"https://www.wikidata.org/wiki/Q166154","display_name":"Line segment","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2312/sca.20141135","is_oa":true,"landing_page_url":"https://doi.org/10.2312/sca.20141135","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.2312/sca.20141135","is_oa":true,"landing_page_url":"https://doi.org/10.2312/sca.20141135","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.43282243609428406,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"work":[1],"introduces":[2],"an":[3,175],"efficient":[4],"method":[5,20],"for":[6,106,112,135],"fully":[7],"automatic":[8],"temporal":[9],"segmentation":[10,137],"of":[11,52,63,92,116,141,157,183,187],"human":[12],"motion":[13,36,65,70,125,151,161,164],"sequences":[14,126],"and":[15,35,72,129,149,166],"similar":[16],"time":[17],"series.":[18],"The":[19,74,95,118],"relies":[21,179],"on":[22,146,180],"a":[23,28,60,184],"neighborhood":[24,80],"graph":[25,81],"to":[26,39,85,98],"partition":[27],"given":[29,42],"data":[30],"sequence":[31],"into":[32,89],"distinct":[33],"activities":[34],"primitives":[37,88],"according":[38],"self-similar":[40],"structures":[41],"in":[43,78,169],"that":[44],"input":[45],"sequence.":[46],"In":[47],"particular,":[48],"the":[49,55,79,108,124,136,147,155,172,181],"fast":[50],"detection":[51],"repetitions":[53],"within":[54],"discovered":[56],"activity":[57],"segments":[58],"is":[59,82,133],"crucial":[61],"problem":[62],"any":[64],"processing":[66],"pipeline":[67],"directed":[68],"at":[69,127],"analysis":[71],"synthesis.":[73],"same":[75,109],"similarity":[76],"information":[77],"further":[83],"exploited":[84],"cluster":[86],"these":[87],"larger":[90],"entities":[91],"semantic":[93],"significance.":[94],"elements":[96],"subject":[97],"this":[99],"classification":[100],"are":[101,144],"then":[102],"used":[103],"as":[104],"prior":[105],"estimating":[107],"target":[110],"values":[111],"entirely":[113],"unknown":[114],"streams":[115],"data.":[117,189],"technique":[119],"makes":[120],"no":[121,130],"assumptions":[122],"about":[123],"hand":[128],"user":[131],"interaction":[132],"required":[134],"or":[138],"clustering.":[139],"Tests":[140],"our":[142,158],"techniques":[143],"conducted":[145],"CMU":[148],"HDM05":[150],"capture":[152],"databases":[153],"demonstrating":[154],"capability":[156],"system":[159],"handling":[160],"segmentation,":[162],"clustering,":[163],"synthesis":[165],"transfer-of-label":[167],"problems":[168],"practice":[170],"-":[171],"latter":[173],"being":[174],"optional":[176],"step":[177],"which":[178],"preexistence":[182],"small":[185],"set":[186],"labeled":[188]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}