{"id":"https://openalex.org/W3113549755","doi":"https://doi.org/10.1109/ivcnz51579.2020.9290606","title":"Machine Learning with Synthetic Data \u2013 a New Way to Learn and Classify the Pictorial Augmented Reality Markers in Real-Time","display_name":"Machine Learning with Synthetic Data \u2013 a New Way to Learn and Classify the Pictorial Augmented Reality Markers in Real-Time","publication_year":2020,"publication_date":"2020-11-25","ids":{"openalex":"https://openalex.org/W3113549755","doi":"https://doi.org/10.1109/ivcnz51579.2020.9290606","mag":"3113549755"},"language":"en","primary_location":{"id":"doi:10.1109/ivcnz51579.2020.9290606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivcnz51579.2020.9290606","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","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/A5029085597","display_name":"Huy Le","orcid":"https://orcid.org/0000-0002-4811-3915"},"institutions":[{"id":"https://openalex.org/I39854758","display_name":"Auckland University of Technology","ror":"https://ror.org/01zvqw119","country_code":"NZ","type":"education","lineage":["https://openalex.org/I39854758"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Huy Le","raw_affiliation_strings":["School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology"],"affiliations":[{"raw_affiliation_string":"School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology","institution_ids":["https://openalex.org/I39854758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042399868","display_name":"Minh Nguyen","orcid":"https://orcid.org/0000-0002-2757-8350"},"institutions":[{"id":"https://openalex.org/I39854758","display_name":"Auckland University of Technology","ror":"https://ror.org/01zvqw119","country_code":"NZ","type":"education","lineage":["https://openalex.org/I39854758"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Minh Nguyen","raw_affiliation_strings":["School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology"],"affiliations":[{"raw_affiliation_string":"School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology","institution_ids":["https://openalex.org/I39854758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064286235","display_name":"Wei Qi Yan","orcid":"https://orcid.org/0009-0006-5891-9919"},"institutions":[{"id":"https://openalex.org/I39854758","display_name":"Auckland University of Technology","ror":"https://ror.org/01zvqw119","country_code":"NZ","type":"education","lineage":["https://openalex.org/I39854758"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Wei Qi Yan","raw_affiliation_strings":["School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology"],"affiliations":[{"raw_affiliation_string":"School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology","institution_ids":["https://openalex.org/I39854758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5029085597"],"corresponding_institution_ids":["https://openalex.org/I39854758"],"apc_list":null,"apc_paid":null,"fwci":0.3908,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.63085636,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T10888","display_name":"Augmented Reality Applications","score":0.9993000030517578,"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/T10888","display_name":"Augmented Reality Applications","score":0.9993000030517578,"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":0.9922999739646912,"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":0.9915000200271606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8172073364257812},{"id":"https://openalex.org/keywords/augmented-reality","display_name":"Augmented reality","score":0.8118189573287964},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.6963385343551636},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6434651613235474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6323936581611633},{"id":"https://openalex.org/keywords/barcode","display_name":"Barcode","score":0.5995998382568359},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5680608749389648},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.527301549911499},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4688972532749176},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4438021779060364},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4106355905532837}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8172073364257812},{"id":"https://openalex.org/C153715457","wikidata":"https://www.wikidata.org/wiki/Q254183","display_name":"Augmented reality","level":2,"score":0.8118189573287964},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6963385343551636},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6434651613235474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6323936581611633},{"id":"https://openalex.org/C2776841711","wikidata":"https://www.wikidata.org/wiki/Q856","display_name":"Barcode","level":2,"score":0.5995998382568359},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5680608749389648},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.527301549911499},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4688972532749176},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4438021779060364},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4106355905532837},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivcnz51579.2020.9290606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivcnz51579.2020.9290606","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W1976127127","https://openalex.org/W2005750530","https://openalex.org/W2025112904","https://openalex.org/W2050843820","https://openalex.org/W2108598243","https://openalex.org/W2109255472","https://openalex.org/W2122122381","https://openalex.org/W2123068464","https://openalex.org/W2124386111","https://openalex.org/W2149003305","https://openalex.org/W2156590962","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2336986501","https://openalex.org/W2578258001","https://openalex.org/W2737015214","https://openalex.org/W2756014408","https://openalex.org/W2778205983","https://openalex.org/W2796347433","https://openalex.org/W2955425717","https://openalex.org/W2963037989","https://openalex.org/W3018757597","https://openalex.org/W3034971973","https://openalex.org/W3035087309","https://openalex.org/W3042011474","https://openalex.org/W3049142938","https://openalex.org/W3128709159","https://openalex.org/W4293584584","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6684191040","https://openalex.org/W6747358120","https://openalex.org/W6750227808","https://openalex.org/W6762718338","https://openalex.org/W6771926570","https://openalex.org/W6777046832","https://openalex.org/W6781798823"],"related_works":["https://openalex.org/W2256570403","https://openalex.org/W2074156223","https://openalex.org/W4391927655","https://openalex.org/W2103246366","https://openalex.org/W2901653204","https://openalex.org/W2102526470","https://openalex.org/W2613659923","https://openalex.org/W4378447789","https://openalex.org/W3207557903","https://openalex.org/W1980504858"],"abstract_inverted_index":{"The":[0,222,245],"idea":[1,208],"of":[2,17,52,112,118,128,131,140,147,149,178,224,250],"Augmented":[3],"Reality":[4],"(AR)":[5],"appeared":[6],"in":[7,38,106,284],"the":[8,30,42,47,53,86,110,116,126,150,162,185,201,214,218,234,241,270],"early":[9],"60s,":[10],"which":[11,263],"recently":[12],"received":[13],"a":[14,97,136,144,169,192,279],"large":[15],"amount":[16],"public":[18],"attention.":[19],"AR":[20,43,67,83,104,202,220,235,267,282],"allows":[21],"us":[22,33,231],"to":[23,45,58,99,232],"work,":[24],"learn,":[25],"play,":[26],"and":[27,36,63,71,91,115,239,290],"connect":[28],"with":[29,143,191],"world":[31],"around":[32],"both":[34],"virtually":[35],"physically":[37],"real-time.":[39],"However,":[40],"picking":[41],"marker":[44,60,203,236,242],"match":[46],"users'":[48],"needs":[49],"is":[50,89,188,248,264],"one":[51,157,182],"most":[54],"challenging":[55],"tasks":[56,130],"due":[57],"different":[59],"encryption/decryption":[61],"methods":[62],"essential":[64],"requirements.":[65],"Barcode":[66],"cards":[68,105],"are":[69],"fast":[70],"efficient,":[72],"but":[73],"they":[74],"do":[75],"not":[76,92],"contain":[77],"much":[78],"visual":[79],"information;":[80],"pictorial":[81],"coloured":[82,160],"card,":[84],"on":[85,269],"other":[87],"hand,":[88],"slow":[90],"reliable.":[93],"This":[94,123],"paper":[95],"proposes":[96],"solution":[98],"obtain":[100],"detectable":[101],"arbitrary":[102],"pictorial/colour":[103],"real-time":[107],"by":[108],"applying":[109],"benefit":[111],"machine":[113],"learning":[114],"power":[117],"synthetic":[119,163,226],"data":[120,164,227],"generation":[121],"techniques.":[122],"technique":[124],"solves":[125],"issue":[127],"labour-intensive":[129],"manual":[132],"annotations":[133],"when":[134],"building":[135],"massive":[137],"training":[138],"dataset":[139,172,187],"deep-learning.":[141],"Thus,":[142],"small":[145],"number":[146],"input":[148],"AR-enhanced":[151],"target":[152],"figures":[153],"(as":[154],"few":[155],"as":[156,200,288],"for":[158,266],"each":[159],"card),":[161],"generated":[165,186,228],"process":[166],"will":[167],"produce":[168],"deep-learning":[170],"trainable":[171],"using":[173,225],"computer-graphic":[174],"rendering":[175],"techniques":[176,229],"(ten":[177],"thousands":[179],"from":[180],"just":[181],"image).":[183],"Second,":[184],"then":[189],"trained":[190,246],"chosen":[193,219],"object":[194],"recognition":[195,237],"convolutional":[196],"neural":[197],"network,":[198],"acting":[199],"tracking":[204],"functionality.":[205],"Our":[206],"proposed":[207],"works":[209],"effectively":[210],"well":[211],"without":[212,260],"modifying":[213],"original":[215],"contents":[216],"(of":[217],"card).":[221],"benefits":[223],"help":[230],"improve":[233],"accuracy":[238],"reduce":[240],"registration":[243],"time.":[244],"model":[247],"capable":[249],"processing":[251],"video":[252],"sequences":[253],"at":[254],"approximately":[255],"25":[256],"frames":[257],"per":[258],"second":[259],"GPU":[261],"Acceleration,":[262],"suitable":[265],"experience":[268],"mobile/web":[271],"platform.":[272],"We":[273],"believed":[274],"that":[275],"it":[276],"could":[277],"be":[278],"promising":[280],"low-cost":[281],"approach":[283],"many":[285],"areas,":[286],"such":[287],"education":[289],"gaming.":[291]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}