{"id":"https://openalex.org/W3088068885","doi":"https://doi.org/10.2352/issn.2470-1173.2020.16.avm-040","title":"Fast Prediction of Contrast Detection Probability","display_name":"Fast Prediction of Contrast Detection Probability","publication_year":2020,"publication_date":"2020-01-26","ids":{"openalex":"https://openalex.org/W3088068885","doi":"https://doi.org/10.2352/issn.2470-1173.2020.16.avm-040","mag":"3088068885"},"language":"en","primary_location":{"id":"doi:10.2352/issn.2470-1173.2020.16.avm-040","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2020.16.avm-040","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-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/A5060976502","display_name":"Robin Jenkin","orcid":"https://orcid.org/0000-0001-6182-2970"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Robin Jenkin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5060976502"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.05534677,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"32","issue":"16","first_page":"40","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9993000030517578,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9991999864578247,"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":0.9987000226974487,"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/pixel","display_name":"Pixel","score":0.8383140563964844},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.7791621685028076},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.716566801071167},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.637681245803833},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5403563976287842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5367563366889954},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4787242114543915},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4719970226287842},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4551834762096405},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.45340248942375183},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3815937638282776},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37950098514556885},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3049347698688507},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.136595219373703},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07864320278167725}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.8383140563964844},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.7791621685028076},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.716566801071167},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.637681245803833},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5403563976287842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5367563366889954},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4787242114543915},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4719970226287842},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4551834762096405},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45340248942375183},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3815937638282776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37950098514556885},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3049347698688507},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.136595219373703},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07864320278167725},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2352/issn.2470-1173.2020.16.avm-040","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2020.16.avm-040","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2069592018","https://openalex.org/W2075740387","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W2358990940","https://openalex.org/W1647606319","https://openalex.org/W4390494008","https://openalex.org/W2046435967","https://openalex.org/W2081596928"],"abstract_inverted_index":{"Contrast":[0],"detection":[1,240],"probability":[2],"(CDP)":[3],"is":[4,50,93,115,124,199,236],"proposed":[5],"as":[6,213],"an":[7],"IEEE":[8],"P2020":[9],"metric":[10],"to":[11,66,80,82,179,190],"predict":[12],"camera":[13,172],"performance":[14,241],"intended":[15],"for":[16,20,37,101,133,182,194,226,242],"computer":[17],"vision":[18],"tasks":[19],"autonomous":[21],"vehicles.":[22],"Its":[23],"calculation":[24,139],"involves":[25,44],"comparing":[26],"combinations":[27,40],"of":[28,35,41,53,56,89,96,147,162,169,186,206,228,244],"pixel":[29,104],"values":[30,164,225],"between":[31,136],"imaged":[32],"patches.":[33],"Computation":[34],"CDP":[36,71,114,148,163,177,224,235],"all":[38],"meaningful":[39],"m":[42],"patches":[43,88,135,227,243],"approximately":[45,83,97,106],"3/2(m2-m).n4":[46],"operations,":[47],"where":[48,121,197],"n":[49],"the":[51,57,112,122,137,141,145,160,191,195,203,238],"length":[52],"one":[54,207],"side":[55],"patch":[58,75,151,208],"in":[59,111],"pixels.":[60],"This":[61,174,232],"work":[62],"presents":[63],"a":[64,94,156,166,183,218],"method":[65],"estimate":[67,146],"Weber":[68,176,223,234],"contrast":[69,198,214],"based":[70,72],"on":[73,149],"individual":[74,150],"statistics":[76],"and":[77,100,140,171,188,209],"thus":[78],"reduces":[79],"computation":[81],"4n2m":[84],"calculations.":[85],"For":[86],"180":[87,102],"10&#xD7;10":[90],"pixels":[91],"this":[92],"reduction":[95],"6500":[98],"times":[99],"25&#xD7;25":[103],"patches,":[105],"41000.":[107],"The":[108],"absolute":[109],"error":[110],"estimated":[113],"less":[116],"than":[117,154],"0.04":[118],"or":[119],"5%":[120],"noise":[123,212],"well":[125],"described":[126],"by":[127,155,201],"Gaussian":[128],"statistics.":[129],"Results":[130],"are":[131],"compared":[132],"simulated":[134],"full":[138],"fast":[142],"estimate.":[143],"Basing":[144],"statistics,":[152],"rather":[153],"pixel-to-pixel":[157],"comparison":[158],"facilitates":[159],"prediction":[161],"from":[165],"physical":[167],"model":[168],"exposure":[170],"conditions.":[173],"allows":[175],"behavior":[178],"be":[180],"investigated":[181],"wide":[184],"variety":[185],"conditions":[187],"leads":[189],"discovery":[192],"that,":[193],"case":[196],"increased":[200],"decreasing":[202],"tone":[204],"value":[205],"therefore":[210],"increasing":[211],"increases,":[215],"there":[216],"exists":[217],"maxima":[219],"which":[220],"yields":[221],"identical":[222],"different":[229,245],"nominal":[230],"contrast.":[231,246],"means":[233],"predicting":[237],"same":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
