{"id":"https://openalex.org/W2971944200","doi":"https://doi.org/10.1109/ccoms.2019.8821676","title":"Data Augmentation Based on 3D Model Data for Machine Learning","display_name":"Data Augmentation Based on 3D Model Data for Machine Learning","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2971944200","doi":"https://doi.org/10.1109/ccoms.2019.8821676","mag":"2971944200"},"language":"en","primary_location":{"id":"doi:10.1109/ccoms.2019.8821676","is_oa":false,"landing_page_url":"http://doi.org/10.1109/ccoms.2019.8821676","pdf_url":null,"source":{"id":"https://openalex.org/S4306498485","display_name":"2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)","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/A5085122996","display_name":"Masumi Iwasaki","orcid":null},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masumi Iwasaki","raw_affiliation_strings":["Graduate School of Computer Science and Engineering, University of Aizu, Aizu-wakamatsu, Fukushima, 965-8580, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Computer Science and Engineering, University of Aizu, Aizu-wakamatsu, Fukushima, 965-8580, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068388221","display_name":"Rentaro Yoshioka","orcid":"https://orcid.org/0000-0003-0858-4867"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Rentaro Yoshioka","raw_affiliation_strings":["Graduate School of Computer Science and Engineering, University of Aizu, Aizu-wakamatsu, Fukushima, 965-8580, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Computer Science and Engineering, University of Aizu, Aizu-wakamatsu, Fukushima, 965-8580, Japan","institution_ids":["https://openalex.org/I141591182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I141591182"],"apc_list":null,"apc_paid":null,"fwci":0.1224,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.42891986,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"2013 cvim 186","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9977999925613403,"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/computer-science","display_name":"Computer science","score":0.7972123026847839},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.7242644429206848},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6804693937301636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6536547541618347},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6437972187995911},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5687068104743958},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5356172323226929},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5353785753250122},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5115097761154175},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5025115013122559},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.45876544713974},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4475020170211792},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43187129497528076},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39202582836151123},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37208932638168335},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08973142504692078},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08913469314575195}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7972123026847839},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.7242644429206848},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6804693937301636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6536547541618347},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6437972187995911},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5687068104743958},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5356172323226929},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5353785753250122},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5115097761154175},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5025115013122559},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.45876544713974},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4475020170211792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43187129497528076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39202582836151123},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37208932638168335},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08973142504692078},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08913469314575195},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccoms.2019.8821676","is_oa":false,"landing_page_url":"http://doi.org/10.1109/ccoms.2019.8821676","pdf_url":null,"source":{"id":"https://openalex.org/S4306498485","display_name":"2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)","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":9,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2088587887","https://openalex.org/W2108598243","https://openalex.org/W2125587358","https://openalex.org/W2568272359","https://openalex.org/W2951284238","https://openalex.org/W2963446712","https://openalex.org/W6631190155","https://openalex.org/W6725739302"],"related_works":["https://openalex.org/W2573606541","https://openalex.org/W2912559722","https://openalex.org/W2786391746","https://openalex.org/W4381430104","https://openalex.org/W2995102745","https://openalex.org/W4226059458","https://openalex.org/W2914559142","https://openalex.org/W1990237101","https://openalex.org/W4285322112","https://openalex.org/W3196471634"],"abstract_inverted_index":{"A":[0],"new":[1],"method":[2,16,81,96,102],"of":[3,20,23,31,38,49,79],"data":[4,12,22,33],"augmentation":[5],"for":[6],"machine":[7],"learning":[8],"using":[9,103],"3D":[10],"model":[11],"is":[13,82],"proposed.":[14],"The":[15,77,95],"involves":[17,46],"the":[18,47,55,62,80],"use":[19,48],"STL":[21],"an":[24],"object":[25],"to":[26,54,61,72],"automatically":[27],"generate":[28],"a":[29,35,69],"set":[30],"training":[32],"covering":[34],"continuous":[36],"range":[37],"view":[39],"angles":[40],"and":[41,59],"various":[42],"backgrounds.":[43],"It":[44],"also":[45],"two":[50],"CNN\u2019s,":[51],"one":[52],"corresponding":[53],"\u2018object":[56],"(parent":[57],"class)\u2019":[58],"another":[60],"\u2018view":[63],"angle":[64],"(child":[65],"class)\u2019,":[66],"that":[67],"provides":[68],"two-stage":[70],"classification":[71],"improve":[73],"tolerance":[74],"against":[75],"over-classification.":[76],"performance":[78],"demonstrated":[83],"by":[84],"comparing":[85],"categorization":[86],"results":[87],"with":[88],"conventional":[89,101],"approach":[90],"based":[91],"on":[92],"real-world":[93],"photographs.":[94],"shows":[97],"satisfactory":[98],"improvements":[99],"over":[100],"photographed":[104],"images.":[105]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}