{"id":"https://openalex.org/W4310007292","doi":"https://doi.org/10.1109/ictc55196.2022.9952946","title":"Comparison of optical flow image preprocessing options for state of the art deep learning models","display_name":"Comparison of optical flow image preprocessing options for state of the art deep learning models","publication_year":2022,"publication_date":"2022-10-19","ids":{"openalex":"https://openalex.org/W4310007292","doi":"https://doi.org/10.1109/ictc55196.2022.9952946"},"language":"en","primary_location":{"id":"doi:10.1109/ictc55196.2022.9952946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc55196.2022.9952946","pdf_url":null,"source":{"id":"https://openalex.org/S4363607740","display_name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","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":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","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/A5073700695","display_name":"Tomislav Dobricki","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Tomislav Dobricki","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I67900169"]},{"raw_affiliation_string":"Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039957034","display_name":"Yonghee Oh","orcid":"https://orcid.org/0000-0002-1083-7774"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yonghee Oh","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I67900169"]},{"raw_affiliation_string":"Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102497376","display_name":"Haeun Ko","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Haeun Ko","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I67900169"]},{"raw_affiliation_string":"Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015463523","display_name":"Taeyun Kim","orcid":"https://orcid.org/0000-0001-6575-8689"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Taeyun Kim","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I67900169"]},{"raw_affiliation_string":"Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357421","display_name":"Dongyoung Kim","orcid":"https://orcid.org/0000-0002-3364-6524"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dongyoung Kim","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I67900169"]},{"raw_affiliation_string":"Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065943331","display_name":"Byung\u2010Woo Hong","orcid":"https://orcid.org/0000-0003-2752-3939"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byung-Woo Hong","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I67900169"]},{"raw_affiliation_string":"Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"585","last_page":"587"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9976999759674072,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9926000237464905,"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/optical-flow","display_name":"Optical flow","score":0.8629388809204102},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.8254491090774536},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7782851457595825},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6594706773757935},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.6317407488822937},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5972737073898315},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5908112525939941},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5621039867401123},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5160679221153259},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48351719975471497},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46433934569358826},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.43416115641593933},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34758687019348145},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12739825248718262},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10078799724578857},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06289717555046082}],"concepts":[{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.8629388809204102},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.8254491090774536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7782851457595825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6594706773757935},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.6317407488822937},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5972737073898315},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5908112525939941},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5621039867401123},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5160679221153259},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48351719975471497},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46433934569358826},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.43416115641593933},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34758687019348145},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12739825248718262},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10078799724578857},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06289717555046082},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc55196.2022.9952946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc55196.2022.9952946","pdf_url":null,"source":{"id":"https://openalex.org/S4363607740","display_name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","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":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1513100184","https://openalex.org/W1578285471","https://openalex.org/W1755205674","https://openalex.org/W2115579991","https://openalex.org/W2259424905","https://openalex.org/W2560474170","https://openalex.org/W2964123261","https://openalex.org/W3109908659","https://openalex.org/W3127140219","https://openalex.org/W3147597595","https://openalex.org/W6630726011"],"related_works":["https://openalex.org/W115686965","https://openalex.org/W2768918307","https://openalex.org/W2110031805","https://openalex.org/W2040020606","https://openalex.org/W4362659915","https://openalex.org/W2113071088","https://openalex.org/W2321543601","https://openalex.org/W3153082147","https://openalex.org/W2899689856","https://openalex.org/W2968833425"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"many":[3],"new":[4],"deep":[5],"learning":[6],"approaches":[7],"for":[8],"solving":[9],"optical":[10,93,147],"flow":[11,94,148],"estimation":[12],"have":[13,22],"been":[14,23],"showing":[15],"impressive":[16],"results.":[17],"But":[18],"as":[19,48,152],"model":[20,112],"sizes":[21,113],"becoming":[24],"larger,":[25],"executing":[26],"them":[27],"became":[28],"a":[29],"task":[30],"that":[31,63,92,119],"requires":[32],"expensive,":[33],"high":[34],"end,":[35],"hardware.":[36],"Because":[37],"most":[38],"of":[39,60,67,77,89,123,129,131,144],"these":[40,68,72],"models":[41],"use":[42],"full":[43],"colour":[44],"RGB":[45],"image":[46,54,125],"pairs":[47],"an":[49,124],"input,":[50],"we":[51],"test":[52],"several":[53],"preprocessing":[55],"methods":[56],"with":[57,100],"the":[58,75,78,121,127,142],"goal":[59],"finding":[61,106],"techniques":[62],"could":[64,107],"alleviate":[65],"some":[66],"inefficiencies.":[69],"We":[70,116],"conducted":[71],"experiments":[73],"using":[74],"state":[76],"art":[79],"GMA":[80],"(Global":[81],"Motion":[82],"Aggregation)":[83],"network":[84],"architecture.":[85],"Our":[86],"results,":[87],"first":[88],"all,":[90],"show":[91],"can":[95],"be":[96,108],"estimated":[97],"equally":[98],"well":[99],"single":[101],"channel":[102],"greyscale":[103],"images,":[104],"this":[105],"used":[109],"to":[110,135],"lower":[111],"in":[114,126,137,141],"general.":[115],"also":[117],"find":[118],"calculating":[120],"derivative":[122],"direction":[128],"one":[130],"its":[132],"axes":[133],"leads":[134],"improvements":[136],"accuracy,":[138],"but":[139],"only":[140],"case":[143],"less":[145],"difficult":[146],"data":[149],"sets":[150],"such":[151],"FlyingChairs":[153],"and":[154],"SIntel-clean.":[155]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
