{"id":"https://openalex.org/W4377995740","doi":"https://doi.org/10.2352/cic.2014.22.1.art00046","title":"Compression of Reflectance Data Using An Evolved Spectral Correlation Profile","display_name":"Compression of Reflectance Data Using An Evolved Spectral Correlation Profile","publication_year":2014,"publication_date":"2014-11-03","ids":{"openalex":"https://openalex.org/W4377995740","doi":"https://doi.org/10.2352/cic.2014.22.1.art00046"},"language":"en","primary_location":{"id":"doi:10.2352/cic.2014.22.1.art00046","is_oa":false,"landing_page_url":"https://doi.org/10.2352/cic.2014.22.1.art00046","pdf_url":null,"source":{"id":"https://openalex.org/S4210193667","display_name":"Color and Imaging Conference","issn_l":"2166-9635","issn":["2166-9635","2169-2629"],"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":"Color and Imaging Conference","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/A5092012122","display_name":"Peter Morovi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peter Morovi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092012123","display_name":"J\u00e1n Morovi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"J\u00e1n Morovi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078458798","display_name":"Michael H. Brill","orcid":"https://orcid.org/0000-0002-9812-0363"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael H. Brill","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084191962","display_name":"Eric Walowit","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eric Walowit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46970021,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"22","issue":"1","first_page":"259","last_page":"264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9830999970436096,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9440000057220459,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computation","display_name":"Computation","score":0.6460195779800415},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.604918897151947},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6020540595054626},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5569536089897156},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5466334223747253},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5226364135742188},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.490474671125412},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.47998902201652527},{"id":"https://openalex.org/keywords/spectral-line","display_name":"Spectral line","score":0.46637800335884094},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44982144236564636},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3372807204723358},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25342682003974915},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12227937579154968}],"concepts":[{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6460195779800415},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.604918897151947},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6020540595054626},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5569536089897156},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5466334223747253},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5226364135742188},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.490474671125412},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.47998902201652527},{"id":"https://openalex.org/C4839761","wikidata":"https://www.wikidata.org/wiki/Q212111","display_name":"Spectral line","level":2,"score":0.46637800335884094},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44982144236564636},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3372807204723358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25342682003974915},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12227937579154968},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2352/cic.2014.22.1.art00046","is_oa":false,"landing_page_url":"https://doi.org/10.2352/cic.2014.22.1.art00046","pdf_url":null,"source":{"id":"https://openalex.org/S4210193667","display_name":"Color and Imaging Conference","issn_l":"2166-9635","issn":["2166-9635","2169-2629"],"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":"Color and Imaging Conference","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/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"Since":[0],"spectral":[1,15,27,79,104,155,162,206,215],"data":[2,80],"is":[3,81,97,110,179,199],"significantly":[4],"higher-dimensional":[5],"than":[6],"colorimetric":[7],"data,":[8],"the":[9,33,103,123,182,188],"choice":[10],"of":[11,35,49,87,102,125,144,149,175,214],"operating":[12],"in":[13,47,59],"a":[14,55,70,75,111,116,141,154,194],"domain":[16],"brings":[17],"memory,":[18],"storage":[19,213],"and":[20,42,91,100,137,190,212],"computational":[21],"throughput":[22],"hits":[23],"with":[24],"it.":[25],"While":[26],"compression":[28,156],"techniques":[29],"exist,":[30],"e.g.,":[31],"on":[32,160],"basis":[34],"Multivariate":[36],"Analysis":[37,41],"(mainly":[38],"Principal":[39],"Component":[40],"related":[43],"methods),":[44],"they":[45],"result":[46,198],"representations":[48],"spectra":[50,131,178],"that":[51,60,120,132,158,164,208],"no":[52,64],"longer":[53,65],"have":[54],"direct":[56,85],"physical":[57],"meaning":[58],"their":[61,171],"individual":[62],"values":[63],"directly":[66],"express":[67],"properties":[68],"at":[69],"specific":[71],"wavelength":[72],"interval.":[73],"As":[74],"result,":[76],"such":[77],"compressed":[78,205],"not":[82],"suitable":[83],"for":[84,122],"application":[86,148],"physically":[88,129,203],"meaningful":[89],"computation":[90],"analysis.":[92],"The":[93,173,196],"framework":[94],"presented":[95],"here":[96],"an":[98,200],"evolution":[99],"extension":[101],"correlation":[105,169],"profile":[106],"published":[107],"before.":[108],"It":[109],"simple":[112],"model,":[113],"driven":[114],"by":[115],"few":[117],"adjustable":[118],"parameters,":[119],"allows":[121],"generation":[124],"nearly":[126],"arbitrary,":[127],"but":[128],"realistic,":[130],"can":[133],"be":[134],"computed":[135],"efficiently,":[136],"are":[138,165],"useful":[139],"over":[140],"wide":[142],"range":[143],"conditions.":[145],"A":[146],"practical":[147],"its":[150],"principles":[151],"then":[152],"includes":[153],"approach":[157],"relies":[159],"discarding":[161],"wavelengths":[163],"most":[166],"redundant,":[167],"given":[168],"to":[170,187],"neighbors.":[172],"goodness":[174],"representing":[176],"realistic":[177],"evaluated":[180],"using":[181],"MIPE":[183],"metric":[184],"as":[185,193],"applied":[186],"SOCS":[189],"other":[191],"databases":[192],"reference.":[195],"end":[197],"efficient,":[201],"yet":[202],"meaningful,":[204],"representation":[207],"benefits":[209],"computation,":[210],"transmission":[211],"content.":[216]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
