{"id":"https://openalex.org/W7160260820","doi":"https://doi.org/10.1109/wacv61042.2026.00102","title":"MIX-based Foreground and Background Patch Augmentation Guided by Physics and Material Properties for X-ray Detection","display_name":"MIX-based Foreground and Background Patch Augmentation Guided by Physics and Material Properties for X-ray Detection","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160260820","doi":"https://doi.org/10.1109/wacv61042.2026.00102"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5100615125","display_name":"Xintong Liu","orcid":"https://orcid.org/0009-0005-8656-699X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xintong Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007417490","display_name":"Dongliang Chang","orcid":"https://orcid.org/0000-0002-4081-3001"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongliang Chang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013480582","display_name":"Yujun Tong","orcid":"https://orcid.org/0009-0001-2699-0300"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujun Tong","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135398992","display_name":"Zhanyu Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanyu Ma","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"977","last_page":"987"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.1875,"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"}},"topics":[{"id":"https://openalex.org/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.1875,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.18709999322891235,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.032099999487400055,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/material-properties","display_name":"Material properties","score":0.46480000019073486},{"id":"https://openalex.org/keywords/background-subtraction","display_name":"Background subtraction","score":0.3774999976158142},{"id":"https://openalex.org/keywords/spectral-properties","display_name":"Spectral properties","score":0.32690000534057617},{"id":"https://openalex.org/keywords/background-noise","display_name":"Background noise","score":0.3043999969959259}],"concepts":[{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.5009999871253967},{"id":"https://openalex.org/C31555180","wikidata":"https://www.wikidata.org/wiki/Q3523867","display_name":"Material properties","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3864000141620636},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.3774999976158142},{"id":"https://openalex.org/C2985906921","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Spectral properties","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3154999911785126},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3000999987125397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.28049999475479126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321470","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1940176186","https://openalex.org/W2055960666","https://openalex.org/W2101563518","https://openalex.org/W2194775991","https://openalex.org/W2574435628","https://openalex.org/W2806070179","https://openalex.org/W2900547866","https://openalex.org/W2914872683","https://openalex.org/W2963299996","https://openalex.org/W2963351448","https://openalex.org/W2964240537","https://openalex.org/W2964241181","https://openalex.org/W2964286876","https://openalex.org/W2989676862","https://openalex.org/W2998508940","https://openalex.org/W3006025454","https://openalex.org/W3035396860","https://openalex.org/W3092955400","https://openalex.org/W3096609285","https://openalex.org/W3176659256","https://openalex.org/W3194248746","https://openalex.org/W3197695291","https://openalex.org/W3212813052","https://openalex.org/W4214667766","https://openalex.org/W4221003007","https://openalex.org/W4286210182","https://openalex.org/W4385584246","https://openalex.org/W4386066630","https://openalex.org/W4387414993","https://openalex.org/W4387885800","https://openalex.org/W4391661435","https://openalex.org/W4392405509","https://openalex.org/W4392903399","https://openalex.org/W4402916168","https://openalex.org/W4405306251","https://openalex.org/W4408100047"],"related_works":[],"abstract_inverted_index":{"The":[0,61],"performance":[1,159],"of":[2,34,64],"deep":[3],"learning-based":[4],"models":[5],"for":[6,179],"X-ray":[7,35,136,183],"prohibited":[8],"item":[9],"detection":[10],"heavily":[11],"relies":[12],"on":[13,135],"large-scale,":[14],"diverse":[15],"datasets,":[16],"which":[17],"are":[18,81],"often":[19],"unavailable.":[20],"While":[21],"data":[22,58],"augmentation":[23,59,171],"offers":[24],"a":[25,56,72,129],"promising":[26],"solution,":[27],"prevalent":[28],"methods":[29],"ignore":[30],"the":[31,100,110,142,176],"fundamental":[32],"principles":[33],"imaging,":[36],"leading":[37],"to":[38,67,112,145],"artifacts":[39],"such":[40],"as":[41],"distorted":[42],"material":[43,86,116],"properties":[44],"and":[45,96,104,125,156],"unnatural":[46],"thickness":[47,131],"perturbations.":[48],"To":[49,84,119],"bridge":[50],"this":[51],"gap,":[52],"we":[53],"present":[54],"MIX,":[55],"physics-grounded":[57],"pipeline.":[60],"core":[62],"idea":[63],"MIX":[65,88,127],"is":[66],"manipulate":[68],"image":[69],"attributes":[70],"in":[71,122,182],"way":[73],"that":[74],"reflects":[75],"real-world":[76],"physical":[77],"variations.":[78],"Our":[79,148,164],"contributions":[80],"twofold:":[82],"(1)":[83],"address":[85],"ambiguity,":[87],"modulates":[89],"foreground":[90],"pseudo-colors":[91],"by":[92,99],"directly":[93],"manipulating":[94],"hue":[95],"saturation,":[97],"informed":[98],"relationship":[101],"between":[102],"color":[103],"effective":[105,170],"atomic":[106],"number.":[107],"This":[108,139],"forces":[109],"model":[111],"learn":[113],"more":[114],"robust":[115],"representations.":[117],"(2)":[118],"simulate":[120],"variations":[121],"object":[123],"density":[124],"thickness,":[126],"introduces":[128],"novel":[130],"perturbation":[132],"technique":[133],"based":[134],"attenuation":[137],"principles.":[138],"significantly":[140],"improves":[141],"model\u2019s":[143],"adaptability":[144],"geometric":[146],"changes.":[147],"proposed":[149],"method":[150],"seamlessly":[151],"integrates":[152],"with":[153],"existing":[154],"detectors":[155],"yields":[157],"substantial":[158],"gains":[160],"across":[161],"multiple":[162],"benchmarks.":[163],"work":[165],"not":[166],"only":[167],"provides":[168],"an":[169],"solution":[172],"but":[173],"also":[174],"highlights":[175],"critical":[177],"need":[178],"domain-specific":[180],"approaches":[181],"computer":[184],"vision.":[185]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-05-06T00:00:00"}