{"id":"https://openalex.org/W612494465","doi":null,"title":"Pattern Recognition: Methods and Applications","display_name":"Pattern Recognition: Methods and Applications","publication_year":2013,"publication_date":"2013-08-23","ids":{"openalex":"https://openalex.org/W612494465","mag":"612494465"},"language":"en","primary_location":{"id":"mag:612494465","doi":null,"is_oa":false,"landing_page_url":"http://ci.nii.ac.jp/ncid/BB14081216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"book","is_traditional":true,"indexed_in":[],"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/A5074599271","display_name":"Khalid Mohamed Hosny","orcid":"https://orcid.org/0000-0001-8065-8977","observed_orcids":["https://orcid.org/0000-0001-8065-8977"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khalid M. Hosny","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8065-8977","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063977138","display_name":"Jorge de la Calleja","orcid":"https://orcid.org/0000-0002-6846-3162","observed_orcids":["https://orcid.org/0000-0002-6846-3162"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jorge de la Calleja","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-6846-3162","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1473,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.54307116,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.357699990272522,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.357699990272522,"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/T10057","display_name":"Face and Expression Recognition","score":0.13040000200271606,"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/T13674","display_name":"Computer Science and Engineering","score":0.1128000020980835,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/pattern-recognition","display_name":"pattern recognition","score":0.9760000109672546},{"id":"https://openalex.org/keywords/outlier-detection","display_name":"outlier detection","score":0.9639999866485596},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"fuzzy logic","score":0.9070000052452087},{"id":"https://openalex.org/keywords/maximum-entropy-principle","display_name":"maximum entropy principle","score":0.9860000014305115},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9900000095367432},{"id":"https://openalex.org/keywords/multi-sensor-fusion","display_name":"multi-sensor fusion","score":0.7580000162124634},{"id":"https://openalex.org/keywords/face-recognition","display_name":"face recognition","score":0.8820000290870667},{"id":"https://openalex.org/keywords/differential-evolution","display_name":"differential evolution","score":0.9259999990463257},{"id":"https://openalex.org/keywords/binary-image-skeletonization","display_name":"binary image skeletonization","score":0.8640000224113464},{"id":"https://openalex.org/keywords/bezier-curves","display_name":"B\u00e9zier curves","score":0.9309999942779541},{"id":"https://openalex.org/keywords/structured-light-3d-reconstruction","display_name":"structured light 3D reconstruction","score":0.9190000295639038},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"pedestrian detection","score":0.7630000114440918},{"id":"https://openalex.org/keywords/adas","display_name":"ADAS","score":0.6919999718666077},{"id":"https://openalex.org/keywords/thesaurus-construction","display_name":"thesaurus construction","score":0.6650000214576721},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"text segmentation","score":0.6399999856948853},{"id":"https://openalex.org/keywords/topic-clustering","display_name":"topic clustering","score":0.35499998927116394},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"image segmentation","score":0.37400001287460327},{"id":"https://openalex.org/keywords/shape-analysis","display_name":"shape analysis","score":0.38199999928474426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6261829137802124},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6218044757843018},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5841546654701233},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42311882972717285},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4169046878814697},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36736759543418884}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:612494465","doi":null,"is_oa":false,"landing_page_url":"http://ci.nii.ac.jp/ncid/BB14081216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4285234557","https://openalex.org/W418482196","https://openalex.org/W7099389804","https://openalex.org/W1820688260","https://openalex.org/W2338165827","https://openalex.org/W3085820219","https://openalex.org/W566709283","https://openalex.org/W2427247435","https://openalex.org/W2132549764","https://openalex.org/W3217207351"],"abstract_inverted_index":{"Pattern":[0],"recognition":[1,180,541],"-":[2],"Methods":[3],"and":[4,11,35,42,55,124,188,196,201,221,251,273,501,535,558],"Applications":[5],"includes":[6],"contributions":[7],"from":[8,129,399,493],"university":[9],"educators":[10],"active":[12],"research":[13],"experts.":[14],"This":[15,439],"book":[16],"is":[17,72,104,145,320,382,396,403,424],"intended":[18],"to":[19,33,76,106,147,233,306,354,405,427,539,555],"serve":[20],"as":[21,241,384,461],"a":[22,48,69,81,115,149,173,238,242,257,269,325,334,342,362,367,385,388,400,412,425,455,480,508],"basic":[23],"reference":[24],"on":[25,29,68,297],"pattern":[26,540],"recognition,":[27],"especially":[28],"the":[30,78,92,96,100,120,125,167,204,218,222,235,275,281,289,299,339,373,406,415,430,434,491,524,527,550],"topics":[31],"related":[32,404],"image":[34,240],"graphics":[36],"processing,":[37,41],"shape":[38],"analysis,":[39],"text":[40,448],"bioinformatics":[43],"analysis.":[44],"Chapter":[45,89,131,170,207,227,254,294,331,364,409,452,477,505,530],"1":[46,250],"proposes":[47,172,209,229,333,366,411,454],"review":[49],"of":[50,80,151,166,237,301,341,387,390,394,414,447,458,464,474,518,526,552],"traditional":[51],"outlier":[52],"detection":[53],"methods":[54,446,537],"their":[56,497],"recent":[57],"enhancements.":[58],"Some":[59],"particular":[60],"data":[61,71],"representations":[62],"are":[63,247],"presented.":[64],"A":[65,140,315,379],"case-study":[66],"based":[67],"synthetic":[70],"proposed":[73,290,422],"in":[74,304,313,359],"order":[75,305],"demonstrate":[77],"potential":[79,300],"fuzzy":[82],"logic":[83],"approach":[84,177,183,511],"which":[85,95,154,395,468],"combines":[86,184],"several":[87],"techniques.":[88],"2":[90,252],"studies":[91],"conditions":[93],"for":[94,135,178,310,322,336,432,472,483,512,549],"solution":[97],"given":[98,108,380,407],"by":[99,109,377,418,444,450],"Maximum":[101],"Entropy":[102],"Principle":[103],"equivalent":[105],"that":[107,118,216,370,402],"Support":[110],"Vector":[111],"Machines.":[112],"It":[113],"describes":[114,532],"unifying":[116],"framework":[117],"computes":[119],"probability":[121],"density":[122],"function":[123],"optimal":[126],"separating":[127],"surface":[128,198,277,291],"examples.":[130],"3":[132],"presents":[133,256,507],"techniques":[134],"building":[136],"multi-sensor":[137],"fusion":[138],"classifiers.":[139],"pairwise":[141],"diversity-based":[142],"ranking":[143],"strategy":[144,426],"introduced":[146],"select":[148],"subset":[150,165],"ensemble":[152],"components,":[153],"when":[155],"combined,":[156],"will":[157,469],"be":[158,286,442,470,545],"more":[159],"diverse":[160],"than":[161],"any":[162],"other":[163,445],"component":[164],"same":[168],"size.":[169],"4":[171],"neural-network-based":[174],"differential":[175,189],"evolution":[176,190],"face":[179],"(FR).":[181],"The":[182,421,488,516],"neural":[185],"network":[186],"classifiers":[187],"updates,":[191],"applies":[192],"both":[193],"2D":[194],"texture":[195],"3D":[197,276,282],"feature":[199],"vectors,":[200],"effectively":[202],"enhance":[203],"FR":[205],"performance.":[206],"5":[208],"an":[210,230,355,546],"efficient":[211,231],"margin-based":[212],"linear":[213,326],"embedding":[214],"method":[215,417],"exploits":[217],"nearest":[219,223],"hit":[220],"miss":[224],"samples":[225],"only.":[226],"6":[228],"algorithm":[232],"construct":[234],"skeleton":[236],"binary":[239,270],"geometric":[243],"graph":[244],"whose":[245],"edges":[246],"Bezier":[248],"curves":[249],"degrees.":[253],"7":[255],"novel":[258],"structured":[259],"light":[260],"means":[261],"with":[262,328],"no":[263],"coding":[264],"procedure":[265,284],"involved.":[266],"By":[267],"projecting":[268],"rhombic":[271],"pattern,":[272],"computing":[274],"normal":[278],"at":[279],"grid-points,":[280],"reconstruction":[283],"can":[285,441,544],"realized":[287],"via":[288],"integration":[292],"methods.":[293],"8":[295],"focuses":[296],"exploring":[298],"virtual":[302],"worlds":[303],"train":[307],"appearance-based":[308],"models":[309],"pedestrian":[311,318],"detections":[312],"ADAS.":[314],"de":[316],"facto":[317],"detector":[319],"used":[321,443,460],"this":[323,349,360],"task:":[324],"SVM":[327],"HOG":[329],"features.":[330],"9":[332],"technique":[335],"partially":[337],"automating":[338],"creation":[340],"large-scale":[343],"dictionary":[344],"or":[345],"corpus.":[346],"More":[347],"specifically,":[348],"involves":[350],"adding":[351],"unknown":[352],"words":[353],"existing":[356],"language":[357],"resource;":[358],"case,":[361],"thesaurus.":[363],"10":[365],"probabilistic":[368],"model":[369],"explicitly":[371],"considers":[372],"document":[374,381,401],"relations":[375],"represented":[376],"links.":[378],"modeled":[383],"mixture":[386],"set":[389],"topic":[391,419],"distributions,":[392],"each":[393],"borrowed":[397],"(cited)":[398],"document.":[408],"11":[410],"improve":[413],"segmentation":[416,449],"ClustSeg.":[420],"improvement":[423],"automatically":[428],"calculate":[429],"threshold":[431],"deciding":[433],"cohesiveness":[435],"between":[436],"textual":[437],"units.":[438],"proposal":[440],"topic.":[451],"12":[453],"comparative":[456],"analysis":[457],"Wavelets,":[459],"input":[462],"attributes":[463],"support":[465],"vector":[466],"machines,":[467],"responsible":[471],"classification":[473,520],"pathological":[475],"voices.":[476],"13":[478],"introduces":[479],"complete":[481],"methodology":[482,489],"automatic":[484],"human":[485],"chromosome":[486],"classification.":[487],"isolates":[490],"chromosomes":[492],"microscopic":[494],"images,":[495],"extracts":[496],"characteristic":[498],"band":[499],"profiles,":[500],"then":[502],"classifies":[503],"them.":[504],"14":[506],"compositional":[509],"spectra":[510],"classifying":[513],"bacterial":[514],"genomes.":[515],"problem":[517],"bacteria":[519],"arose":[521],"long":[522],"before":[523],"start":[525],"Genomic":[528],"Era.":[529],"15":[531],"how":[533],"spectroscopic":[534],"chromatographic":[536],"coupled":[538],"multivariate":[542],"algorithms":[543],"excellent":[547],"tool":[548],"determination":[551,560],"fuel":[553],"compliance":[554],"technical":[556],"specifications":[557],"origin":[559],"purposes.":[561]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-10-25T09:29:13.331239","created_date":"2016-06-24T00:00:00"}