{"id":"https://openalex.org/W7131122807","doi":"https://doi.org/10.1109/iccvw69036.2025.00745","title":"Multimodal Fusion of X-Ray Transmission and Dark Field Imaging for Apple Internal Disorders Detection","display_name":"Multimodal Fusion of X-Ray Transmission and Dark Field Imaging for Apple Internal Disorders Detection","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131122807","doi":"https://doi.org/10.1109/iccvw69036.2025.00745"},"language":"en","primary_location":{"id":"doi:10.1109/iccvw69036.2025.00745","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00745","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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/A5049102674","display_name":"Jiaqi He","orcid":"https://orcid.org/0000-0003-3148-3829"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Jiaqi He","raw_affiliation_strings":["KU Leuven,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KU Leuven,Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077347741","display_name":"Astrid Tempelaere","orcid":"https://orcid.org/0000-0003-3807-6695"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Astrid Tempelaere","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029225893","display_name":"Janne Vignero","orcid":"https://orcid.org/0000-0003-0637-6618"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Janne Vignero","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122292581","display_name":"Pieter VERBOVEN","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pieter Verboven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126607653","display_name":"Bart Nicolai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bart Nicolai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.83078803,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7229","last_page":"7238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13044","display_name":"Biofield Effects and Biophysics","score":0.23479999601840973,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T13044","display_name":"Biofield Effects and Biophysics","score":0.23479999601840973,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.14640000462532043,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.06880000233650208,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.6126000285148621},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5611000061035156},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5557000041007996},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5256999731063843},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.5248000025749207},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.46309998631477356},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4320000112056732},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3831999897956848}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6873999834060669},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.6126000285148621},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5939000248908997},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5611000061035156},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5557000041007996},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.5248000025749207},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.46309998631477356},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44830000400543213},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3831999897956848},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.38109999895095825},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3188999891281128},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.28679999709129333},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00745","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00745","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:20.500.12942/782817","is_oa":false,"landing_page_url":"https://lirias.kuleuven.be/handle/20.500.12942/782817","pdf_url":null,"source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","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":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"2025 International Conference on Computer Vision Workshops-ICCVW, HI, Honolulu, 19-20 October 2025","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6299898293","display_name":null,"funder_award_id":"S003421N,ISH8Q24N","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321730","display_name":"Fonds Wetenschappelijk Onderzoek","ror":"https://ror.org/03qtxy027"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1789886147","https://openalex.org/W1967911785","https://openalex.org/W1967939756","https://openalex.org/W1984557106","https://openalex.org/W2000674093","https://openalex.org/W2028027151","https://openalex.org/W2097117768","https://openalex.org/W2107449292","https://openalex.org/W2122111042","https://openalex.org/W2133059825","https://openalex.org/W2168366932","https://openalex.org/W2194775991","https://openalex.org/W2466344401","https://openalex.org/W2523986009","https://openalex.org/W2808157660","https://openalex.org/W2903950532","https://openalex.org/W2905396090","https://openalex.org/W2922522000","https://openalex.org/W3006142809","https://openalex.org/W3126442124","https://openalex.org/W3155598205","https://openalex.org/W3159481202","https://openalex.org/W3185535538","https://openalex.org/W3216623900","https://openalex.org/W4206042814","https://openalex.org/W4210385745","https://openalex.org/W4223899585","https://openalex.org/W4223947552","https://openalex.org/W4239510810","https://openalex.org/W4295217294","https://openalex.org/W4313398454","https://openalex.org/W4386631551","https://openalex.org/W4387473692","https://openalex.org/W4390190432","https://openalex.org/W4394755310","https://openalex.org/W4396613605","https://openalex.org/W4396669176","https://openalex.org/W4399322721","https://openalex.org/W4400943505","https://openalex.org/W4401495975","https://openalex.org/W4406088416","https://openalex.org/W4416005636"],"related_works":[],"abstract_inverted_index":{"Apples":[0],"are":[1],"typically":[2],"harvested":[3],"once":[4],"a":[5,44,131],"year":[6],"and":[7,23,37,77,85,92,99,114,123,141,165],"stored":[8],"under":[9],"controlled":[10],"atmosphere":[11],"conditions":[12,113],"to":[13,60,161,173],"preserve":[14],"quality.":[15],"However,":[16],"internal":[17,21,49,178],"defects":[18],"such":[19],"as":[20,43],"browning":[22],"cavities":[24],"can":[25],"develop":[26],"during":[27],"storage.":[28],"X-ray":[29,96],"phase":[30],"contrast":[31],"imaging,":[32],"which":[33],"simultaneously":[34],"captures":[35],"transmission":[36,100],"dark":[38,97],"field":[39,98],"modalities,":[40],"has":[41],"emerged":[42],"promising":[45],"non-destructive":[46],"technique":[47],"for":[48,87,143],"quality":[50,179],"assessment.":[51],"These":[52],"two":[53,106],"complementary":[54],"modalities":[55],"require":[56],"effective":[57],"fusion":[58,72,117,160],"strategies":[59],"be":[61,171],"fully":[62],"leveraged":[63],"in":[64],"automated":[65],"inspection":[66],"systems.":[67],"This":[68],"study":[69],"investigates":[70],"three":[71],"strategies,":[73],"namely":[74],"pixel-level,":[75],"feature-level,":[76],"decision-level,":[78],"combined":[79],"with":[80,158],"multiple":[81],"feature":[82,121,156],"extraction":[83],"methods":[84],"classifiers":[86],"binary":[88],"classification":[89,163],"of":[90,134,153],"healthy":[91],"defective":[93],"apples":[94],"using":[95,118],"images.":[101],"Models":[102],"were":[103],"evaluated":[104],"on":[105,136],"test":[107,139,146],"sets":[108],"from":[109],"different":[110],"seasons,":[111],"storage":[112],"durations.":[115],"Feature-level":[116],"DINO":[119],"ViT-B/8":[120],"extractor":[122],"logistic":[124],"regression":[125],"achieved":[126],"the":[127,137,151],"highest":[128],"performance,":[129],"reaching":[130],"balanced":[132],"accuracy":[133],"0.92":[135],"within-distribution":[138],"set":[140],"0.94":[142],"another":[144],"independent":[145],"set.":[147],"The":[148,167],"findings":[149],"highlight":[150],"importance":[152],"combining":[154],"robust":[155],"representations":[157],"multimodal":[159],"improve":[162],"performance":[164],"generalization.":[166],"proposed":[168],"framework":[169],"could":[170],"extended":[172],"other":[174],"horticultural":[175],"products":[176],"exhibiting":[177],"disorders.":[180]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-24T00:00:00"}