{"id":"https://openalex.org/W4386590530","doi":"https://doi.org/10.1109/icip49359.2023.10222434","title":"Self-Supervised Learning for Scanned Halftone Classification with Novel Augmentation Techniques","display_name":"Self-Supervised Learning for Scanned Halftone Classification with Novel Augmentation Techniques","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386590530","doi":"https://doi.org/10.1109/icip49359.2023.10222434"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222434","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10222434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5089868194","display_name":"Jing-Ming Guo","orcid":"https://orcid.org/0000-0002-8041-6326"},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Jing-Ming Guo","raw_affiliation_strings":["National Taiwan University of Science and Technology,Department of Electrical Engineering and Advanced Intelligent Image and Vision Technology Research Center,Taipei,Taiwan,10607"],"affiliations":[{"raw_affiliation_string":"National Taiwan University of Science and Technology,Department of Electrical Engineering and Advanced Intelligent Image and Vision Technology Research Center,Taipei,Taiwan,10607","institution_ids":["https://openalex.org/I154864474"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090893271","display_name":"Sankarasrinivasan Seshathiri","orcid":"https://orcid.org/0000-0002-3738-3602"},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Sankarasrinivasan Seshathiri","raw_affiliation_strings":["National Taiwan University of Science and Technology,Department of Electrical Engineering and Advanced Intelligent Image and Vision Technology Research Center,Taipei,Taiwan,10607"],"affiliations":[{"raw_affiliation_string":"National Taiwan University of Science and Technology,Department of Electrical Engineering and Advanced Intelligent Image and Vision Technology Research Center,Taipei,Taiwan,10607","institution_ids":["https://openalex.org/I154864474"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5089868194"],"corresponding_institution_ids":["https://openalex.org/I154864474"],"apc_list":null,"apc_paid":null,"fwci":0.2077,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52916827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"13","issue":null,"first_page":"2650","last_page":"2654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11666","display_name":"Color Science and Applications","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11666","display_name":"Color Science and Applications","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.995199978351593,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/halftone","display_name":"Halftone","score":0.9950965642929077},{"id":"https://openalex.org/keywords/dither","display_name":"Dither","score":0.8152210116386414},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.789914071559906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7889224290847778},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5469558238983154},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.487411767244339},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4613049626350403},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.44621849060058594},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4393256604671478},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4368533492088318},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.42629289627075195},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3621435761451721},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3614305853843689},{"id":"https://openalex.org/keywords/noise-shaping","display_name":"Noise shaping","score":0.10215723514556885},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09527099132537842}],"concepts":[{"id":"https://openalex.org/C2777635815","wikidata":"https://www.wikidata.org/wiki/Q1110021","display_name":"Halftone","level":3,"score":0.9950965642929077},{"id":"https://openalex.org/C70451592","wikidata":"https://www.wikidata.org/wiki/Q376493","display_name":"Dither","level":3,"score":0.8152210116386414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.789914071559906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7889224290847778},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5469558238983154},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.487411767244339},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4613049626350403},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.44621849060058594},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4393256604671478},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4368533492088318},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.42629289627075195},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3621435761451721},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3614305853843689},{"id":"https://openalex.org/C9083635","wikidata":"https://www.wikidata.org/wiki/Q2133535","display_name":"Noise shaping","level":2,"score":0.10215723514556885},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09527099132537842},{"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/icip49359.2023.10222434","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10222434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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":27,"referenced_works":["https://openalex.org/W2023368606","https://openalex.org/W2051098285","https://openalex.org/W2053717006","https://openalex.org/W2078759169","https://openalex.org/W2097548606","https://openalex.org/W2111072639","https://openalex.org/W2148936814","https://openalex.org/W2163677385","https://openalex.org/W2189344795","https://openalex.org/W2283340597","https://openalex.org/W2407088632","https://openalex.org/W2803218289","https://openalex.org/W2961018736","https://openalex.org/W3005680577","https://openalex.org/W3035060554","https://openalex.org/W3094600839","https://openalex.org/W3108870912","https://openalex.org/W3134652006","https://openalex.org/W3163290956","https://openalex.org/W3171007011","https://openalex.org/W3212813052","https://openalex.org/W4236789552","https://openalex.org/W6682019870","https://openalex.org/W6774314701","https://openalex.org/W6779326418","https://openalex.org/W6791742336","https://openalex.org/W6803791761"],"related_works":["https://openalex.org/W2147758750","https://openalex.org/W2169172224","https://openalex.org/W2762579005","https://openalex.org/W2020245493","https://openalex.org/W1993315740","https://openalex.org/W1749173910","https://openalex.org/W764555139","https://openalex.org/W2543386930","https://openalex.org/W2116595504","https://openalex.org/W2041774888"],"abstract_inverted_index":{"The":[0,94],"current":[1],"halftone":[2,22,42,49,108,117],"classification":[3,118],"models":[4,36],"use":[5],"supervised":[6],"learning,":[7],"which":[8],"requires":[9],"a":[10,33,55,101],"large":[11],"dataset":[12],"of":[13],"labeled":[14],"images.":[15,50],"However,":[16],"in":[17],"practical":[18],"situations,":[19],"the":[20,91,112,121],"source":[21],"type":[23],"is":[24,97],"often":[25],"unknown,":[26],"making":[27],"it":[28],"difficult":[29],"to":[30,85],"create":[31],"such":[32],"dataset.":[34],"These":[35],"are":[37],"typically":[38],"trained":[39],"on":[40,47,65],"synthetic":[41],"images":[43],"and":[44,69,88],"perform":[45],"poorly":[46],"scanned":[48],"To":[51],"address":[52],"this":[53],"issue,":[54],"new":[56],"self-supervised":[57],"learning":[58],"(SSL)":[59],"model":[60,96,105,114],"has":[61,82],"been":[62,83],"proposed,":[63],"based":[64],"Barlow":[66],"Twins":[67],"(BT)":[68],"Blue":[70],"Noise":[71],"(BN)":[72],"dithering.":[73],"In":[74,110],"addition,":[75],"an":[76],"effective":[77],"patch":[78],"swapping":[79],"augmentation":[80],"technique":[81],"developed":[84],"improve":[86],"accuracy":[87],"speed":[89],"up":[90],"training":[92],"process.":[93],"pre-trained":[95],"then":[98],"fine-tuned":[99],"using":[100],"modified":[102],"progressive":[103],"multi-granularity":[104],"with":[106],"limited":[107],"labels.":[109],"overall,":[111],"proposed":[113],"outperforms":[115],"existing":[116],"algorithms,":[119],"becoming":[120],"state-of-the-art":[122],"method.":[123]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}