{"id":"https://openalex.org/W4363647250","doi":"https://doi.org/10.1109/ciss56502.2023.10089678","title":"When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features","display_name":"When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features","publication_year":2023,"publication_date":"2023-03-22","ids":{"openalex":"https://openalex.org/W4363647250","doi":"https://doi.org/10.1109/ciss56502.2023.10089678"},"language":"en","primary_location":{"id":"doi:10.1109/ciss56502.2023.10089678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss56502.2023.10089678","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","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/A5088847033","display_name":"Daniel Deniz","orcid":"https://orcid.org/0000-0002-0313-2127"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Daniel Deniz","raw_affiliation_strings":["University of Granada,Dep. of Computer Architecture and Technology,Spain","Dep. of Computer Architecture and Technology, University of Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Granada,Dep. of Computer Architecture and Technology,Spain","institution_ids":["https://openalex.org/I173304897"]},{"raw_affiliation_string":"Dep. of Computer Architecture and Technology, University of Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101663021","display_name":"Eduardo Ros","orcid":"https://orcid.org/0000-0001-6613-5256"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Eduardo Ros","raw_affiliation_strings":["University of Granada,Dep. of Computer Architecture and Technology,Spain","Dep. of Computer Architecture and Technology, University of Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Granada,Dep. of Computer Architecture and Technology,Spain","institution_ids":["https://openalex.org/I173304897"]},{"raw_affiliation_string":"Dep. of Computer Architecture and Technology, University of Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018314134","display_name":"Cornelia Fermller","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cornelia Fermller","raw_affiliation_strings":["University of Maryland,Perception and Robotics Group,USA","Perception and Robotics Group, University of Maryland, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,Perception and Robotics Group,USA","institution_ids":[]},{"raw_affiliation_string":"Perception and Robotics Group, University of Maryland, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001763650","display_name":"Francisco Barranco","orcid":"https://orcid.org/0000-0002-3721-0170"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Francisco Barranco","raw_affiliation_strings":["University of Granada,Dep. of Computer Architecture and Technology,Spain","Dep. of Computer Architecture and Technology, University of Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Granada,Dep. of Computer Architecture and Technology,Spain","institution_ids":["https://openalex.org/I173304897"]},{"raw_affiliation_string":"Dep. of Computer Architecture and Technology, University of Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2452,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.92183039,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9940999746322632,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9929999709129333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8491009473800659},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7263734936714172},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6494476795196533},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.6394491195678711},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6265959739685059},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5806550979614258},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5697518587112427},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5272188186645508},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.454018235206604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.29923397302627563},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24004176259040833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8491009473800659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7263734936714172},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6494476795196533},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.6394491195678711},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6265959739685059},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5806550979614258},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5697518587112427},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5272188186645508},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.454018235206604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29923397302627563},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24004176259040833},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ciss56502.2023.10089678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss56502.2023.10089678","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","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":32,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1947481528","https://openalex.org/W2040324237","https://openalex.org/W2157331557","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2318145034","https://openalex.org/W2469278928","https://openalex.org/W2528383574","https://openalex.org/W2745933219","https://openalex.org/W2765218414","https://openalex.org/W2805029945","https://openalex.org/W2810685774","https://openalex.org/W2883780447","https://openalex.org/W2921082809","https://openalex.org/W2943602317","https://openalex.org/W2950380208","https://openalex.org/W2979969178","https://openalex.org/W3011302802","https://openalex.org/W3040838455","https://openalex.org/W3087591564","https://openalex.org/W3092366396","https://openalex.org/W3100465494","https://openalex.org/W3128431487","https://openalex.org/W3134123147","https://openalex.org/W4206780588","https://openalex.org/W4225985385","https://openalex.org/W4246193833","https://openalex.org/W4292787316","https://openalex.org/W4385328207","https://openalex.org/W6606171110","https://openalex.org/W6762718338"],"related_works":["https://openalex.org/W2986579802","https://openalex.org/W3108691306","https://openalex.org/W4389237622","https://openalex.org/W2166309310","https://openalex.org/W4385753159","https://openalex.org/W4387251107","https://openalex.org/W4214914769","https://openalex.org/W4200152843","https://openalex.org/W17155033","https://openalex.org/W3033201189"],"abstract_inverted_index":{"Visual":[0],"event":[1,65,110],"sensors":[2,111],"only":[3,158],"output":[4],"data":[5],"when":[6,50],"changes":[7],"in":[8,32,104,182],"the":[9,21,33,91,97,100,113,138,153,160],"scene":[10,22],"happen":[11],"at":[12],"very":[13,179],"high":[14],"frequency.":[15],"This":[16],"allows":[17],"for":[18,41,68,116],"smartly":[19],"compressing":[20],"and":[23,77,87,94,96,134,169,172],"thus,":[24],"enabling":[25],"real-time":[26],"operation.":[27],"Despite":[28],"these":[29,42,118],"advantages,":[30],"works":[31],"literature":[34],"have":[35],"struggled":[36],"to":[37,46,122,136,151,155],"show":[38,59,146],"a":[39,60,149,178],"niche":[40],"event-driven":[43],"approaches":[44,107,124],"compared":[45],"conventional":[47,123],"sensors,":[48],"especially":[49],"focusing":[51],"on":[52,109,126],"accuracy":[53,76],"performance.":[54,79],"In":[55],"this":[56],"work,":[57,106],"we":[58,145,162],"case":[61],"that":[62,166],"fully":[63],"exploits":[64],"sensor":[66],"advantages:":[67],"manipulation":[69,83],"action":[70],"recognition,":[71],"learning":[72,88],"events":[73],"achieves":[74,173],"superior":[75],"time":[78,175],"The":[80],"recognition":[81],"of":[82,142],"actions":[84],"requires":[85],"extracting":[86,117],"features":[89,120,133,154],"from":[90,140],"hand":[92],"pose":[93],"trajectory":[95],"interaction":[98],"with":[99,177],"object.":[101],"As":[102],"shown":[103],"our":[105],"based":[108,125],"are":[112],"best":[114],"fit":[115],"dynamic":[119],"contrarily":[121],"full":[127],"frames,":[128],"which":[129],"mostly":[130],"extract":[131,152],"spatial":[132],"need":[135],"reconstruct":[137],"dynamics":[139],"sequences":[141],"frames.":[143],"Finally,":[144],"how":[147],"using":[148],"tracker":[150],"be":[156],"learned":[157],"around":[159],"hand,":[161],"obtain":[163],"an":[164],"approach":[165],"is":[167],"scene-":[168],"almost":[170],"object-agnostic":[171],"good":[174],"performance":[176],"limited":[180],"impact":[181],"accuracy.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}