{"id":"https://openalex.org/W1582149471","doi":"https://doi.org/10.1109/i2mtc.2015.7151421","title":"Heterogeneous feature fusion-based optimal face image acquisition in visual sensor network","display_name":"Heterogeneous feature fusion-based optimal face image acquisition in visual sensor network","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1582149471","doi":"https://doi.org/10.1109/i2mtc.2015.7151421","mag":"1582149471"},"language":"en","primary_location":{"id":"doi:10.1109/i2mtc.2015.7151421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc.2015.7151421","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings","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/A5004114954","display_name":"Kuicheng Lin","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":true,"raw_author_name":"Kuicheng Lin","raw_affiliation_strings":["Department of Precision Instrument, Tsinghua University, Beijing, China","State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University , Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Precision Instrument, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University , Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035550613","display_name":"Xue Wang","orcid":"https://orcid.org/0000-0003-4842-3160"},"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":"Xue Wang","raw_affiliation_strings":["Department of Precision Instrument, Tsinghua University, Beijing, China","State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University , Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Precision Instrument, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University , Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048137261","display_name":"Sujin Cui","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":"Sujin Cui","raw_affiliation_strings":["Department of Precision Instrument, Tsinghua University, Beijing, China","State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University , Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Precision Instrument, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University , Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037490613","display_name":"Yuqi Tan","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":"Yuqi Tan","raw_affiliation_strings":["Department of Precision Instrument, Tsinghua University, Beijing, China","State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University , Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Precision Instrument, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University , Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5004114954"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.7364,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.77938763,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"67","issue":null,"first_page":"1078","last_page":"1083"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9939000010490417,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9939000010490417,"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/T11448","display_name":"Face recognition and analysis","score":0.9915000200271606,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9889000058174133,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7821458578109741},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7733669877052307},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7211343050003052},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5518656969070435},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.535760760307312},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5051007866859436},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4806296229362488},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.444293737411499},{"id":"https://openalex.org/keywords/gaussian-blur","display_name":"Gaussian blur","score":0.4405750334262848},{"id":"https://openalex.org/keywords/face-detection","display_name":"Face detection","score":0.4115496277809143},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.40309247374534607},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3861452341079712},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2971358299255371},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19492590427398682},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.10732096433639526}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7821458578109741},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7733669877052307},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7211343050003052},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5518656969070435},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.535760760307312},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5051007866859436},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4806296229362488},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.444293737411499},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.4405750334262848},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.4115496277809143},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.40309247374534607},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3861452341079712},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2971358299255371},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19492590427398682},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.10732096433639526},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/i2mtc.2015.7151421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc.2015.7151421","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2002607567","https://openalex.org/W2003251444","https://openalex.org/W2025164974","https://openalex.org/W2029011405","https://openalex.org/W2047508432","https://openalex.org/W2079020532","https://openalex.org/W2094540721","https://openalex.org/W2100615755","https://openalex.org/W2108708638","https://openalex.org/W2109599521","https://openalex.org/W2146454619","https://openalex.org/W2166607659","https://openalex.org/W2170567421","https://openalex.org/W6662335928"],"related_works":["https://openalex.org/W2103761320","https://openalex.org/W2358167375","https://openalex.org/W116564642","https://openalex.org/W4386563447","https://openalex.org/W1983380679","https://openalex.org/W1981649169","https://openalex.org/W2357354286","https://openalex.org/W2388705266","https://openalex.org/W2336272890","https://openalex.org/W4308999381"],"abstract_inverted_index":{"High":[0],"quality":[1,56,84,92,115,148],"face":[2,22,26,35,48,55,78,82,91,104,114,131,147,161],"image":[3,36,132],"acquisition":[4,37,50],"from":[5],"huge":[6],"video":[7],"data":[8,60],"obtained":[9],"in":[10,18,39,108],"visual":[11,40,109],"sensor":[12,41,110],"network":[13],"is":[14,44,87,138],"of":[15,77,103,126],"great":[16],"significance":[17],"applications":[19],"related":[20],"to":[21,99,140,144],"processing,":[23],"such":[24],"as":[25],"recognition":[27],"and":[28,51,62,71,129,164],"reconstruction.":[29],"This":[30],"paper":[31],"proposes":[32],"an":[33],"optimal":[34,160],"method":[38,86,157],"network,":[42],"which":[43],"based":[45,121],"on":[46,122],"collaborative":[47,68],"frames":[49,83],"heterogeneous":[52,123],"feature":[53,124],"fusion-based":[54],"assessment.":[57],"Gaussian-probability-distribution-based":[58],"multi-view":[59],"fusion":[61,125],"kalman":[63],"filter":[64],"are":[65,97,120],"used":[66,139],"for":[67],"target":[69],"localization":[70],"tracking.":[72],"To":[73],"achieve":[74],"primary":[75],"screening":[76,102],"frames,":[79],"a":[80,146],"lightweight":[81],"evaluation":[85],"presented.":[88],"Importantly,":[89],"new":[90,113],"assessment":[93,116,149],"criterion":[94,117],"calculation":[95,118],"methods":[96,119],"proposed":[98,156],"make":[100],"fine":[101],"images":[105,162],"more":[106],"applicable":[107],"network.":[111],"The":[112],"pedestrian":[127],"tracking":[128],"static":[130],"features":[133],"analysis.":[134],"Fuzzy":[135],"inference":[136],"engine":[137],"combine":[141],"these":[142],"criteria":[143],"generate":[145],"score.":[150],"Experimental":[151],"results":[152],"show":[153],"that":[154],"the":[155],"can":[158],"acquire":[159],"accurately":[163],"robustly.":[165]},"counts_by_year":[{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}