{"id":"https://openalex.org/W3161101984","doi":"https://doi.org/10.1109/icpr48806.2021.9413038","title":"Automatic Detection of Stationary Waves in the Venus Atmosphere Using Deep Generative Models","display_name":"Automatic Detection of Stationary Waves in the Venus Atmosphere Using Deep Generative Models","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3161101984","doi":"https://doi.org/10.1109/icpr48806.2021.9413038","mag":"3161101984"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9413038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th 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/A5019800133","display_name":"Minori Narita","orcid":"https://orcid.org/0000-0003-2808-6056"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Minori Narita","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047548171","display_name":"Daiki Kimura","orcid":"https://orcid.org/0000-0001-5180-1949"},"institutions":[{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Daiki Kimura","raw_affiliation_strings":["IBM Research AI, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"IBM Research AI, Tokyo, Japan","institution_ids":["https://openalex.org/I4210145865"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054965366","display_name":"Takeshi Imamura","orcid":"https://orcid.org/0000-0002-9470-4492"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Imamura","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019800133"],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05729076,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2912","last_page":"2919"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9686999917030334,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T13650","display_name":"Computational Physics and Python Applications","score":0.9124000072479248,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.840888261795044},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.641463577747345},{"id":"https://openalex.org/keywords/venus","display_name":"Venus","score":0.6060501933097839},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6047201752662659},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5409932732582092},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5326831936836243},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4276149570941925},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.41340917348861694},{"id":"https://openalex.org/keywords/geophysics","display_name":"Geophysics","score":0.379124253988266},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3674848675727844},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2790452539920807},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14453816413879395},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0958845317363739}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.840888261795044},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.641463577747345},{"id":"https://openalex.org/C2779900269","wikidata":"https://www.wikidata.org/wiki/Q1408724","display_name":"Venus","level":2,"score":0.6060501933097839},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6047201752662659},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5409932732582092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5326831936836243},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4276149570941925},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.41340917348861694},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.379124253988266},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3674848675727844},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2790452539920807},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14453816413879395},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0958845317363739},{"id":"https://openalex.org/C87355193","wikidata":"https://www.wikidata.org/wiki/Q411","display_name":"Astrobiology","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9413038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th 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":35,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1710476689","https://openalex.org/W1959608418","https://openalex.org/W1983258245","https://openalex.org/W2068090667","https://openalex.org/W2092162592","https://openalex.org/W2122646361","https://openalex.org/W2127979711","https://openalex.org/W2163605009","https://openalex.org/W2295107390","https://openalex.org/W2531327146","https://openalex.org/W2573518834","https://openalex.org/W2771812449","https://openalex.org/W2789159078","https://openalex.org/W2895383329","https://openalex.org/W2941036927","https://openalex.org/W2949608135","https://openalex.org/W2962858109","https://openalex.org/W2964167449","https://openalex.org/W3009146599","https://openalex.org/W3103275553","https://openalex.org/W3153872861","https://openalex.org/W4288409681","https://openalex.org/W4293321333","https://openalex.org/W4298289240","https://openalex.org/W6637568146","https://openalex.org/W6640963894","https://openalex.org/W6684191040","https://openalex.org/W6687506355","https://openalex.org/W6728622933","https://openalex.org/W6748366383","https://openalex.org/W6755076124","https://openalex.org/W6760911107","https://openalex.org/W6786219559","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W3017266184","https://openalex.org/W2918377632","https://openalex.org/W3202913553","https://openalex.org/W3194885736","https://openalex.org/W3046391934","https://openalex.org/W4363671829","https://openalex.org/W2806741695"],"abstract_inverted_index":{"Various":[0],"anomaly":[1,14,42,63,103,130,142],"detection":[2,15,43,64,143],"methods":[3,44,126],"utilizing":[4,62],"different":[5],"types":[6],"of":[7,19,40,57,89,110,137],"images":[8,58,131],"have":[9],"recently":[10],"been":[11],"proposed.":[12],"However,":[13],"in":[16,34],"the":[17,27,35,87,90,108,124,129,135],"field":[18],"planetary":[20],"science":[21,149],"is":[22,32,65],"still":[23],"done":[24],"predominantly":[25],"by":[26,93],"human":[28],"eye":[29],"because":[30],"explainability":[31],"crucial":[33],"physical":[36,148],"sciences":[37],"and":[38,99],"most":[39],"today's":[41],"based":[45],"on":[46,86],"deep":[47],"learning":[48],"cannot":[49],"offer":[50],"enough.":[51],"Moreover,":[52],"preparing":[53],"a":[54,74,95],"large":[55,80],"number":[56],"required":[59],"for":[60],"fully":[61],"not":[66],"always":[67],"feasible.":[68],"In":[69],"this":[70,138],"work,":[71],"we":[72,140],"propose":[73],"new":[75],"framework":[76],"that":[77,116],"automatically":[78],"detects":[79],"bow-shaped":[81],"structures":[82],"(stationary":[83],"waves)":[84],"appearing":[85],"surface":[88],"Venus":[91],"clouds":[92],"applying":[94],"variational":[96],"auto-encoder":[97],"(VAE)":[98],"attention":[100],"maps":[101],"to":[102,147],"detection.":[104],"We":[105],"also":[106],"discuss":[107,141],"advantages":[109],"using":[111],"image":[112],"augmentation.":[113],"Experiments":[114],"show":[115],"our":[117],"approach":[118],"can":[119],"achieve":[120],"higher":[121],"accuracy":[122],"than":[123],"state-of-the-art":[125],"even":[127],"when":[128],"are":[132],"scarce.":[133],"On":[134],"basis":[136],"finding,":[139],"frameworks":[144],"particularly":[145],"suited":[146],"domains.":[150]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
