{"id":"https://openalex.org/W2123150195","doi":"https://doi.org/10.1109/icip.2008.4712243","title":"A covariance adjustment method in compressed domain for noisy image segmentation","display_name":"A covariance adjustment method in compressed domain for noisy image segmentation","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2123150195","doi":"https://doi.org/10.1109/icip.2008.4712243","mag":"2123150195"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2008.4712243","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2008.4712243","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 15th IEEE International Conference on Image Processing","raw_type":"proceedings-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/A5054626081","display_name":"Kyungsuk Pyun","orcid":null},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kyungsuk Pyun","raw_affiliation_strings":["IPG, Hewlett Packard Company, San Diego, CA, USA","[IPG, Hewlett-Packard Co., San Diego, CA]"],"affiliations":[{"raw_affiliation_string":"IPG, Hewlett Packard Company, San Diego, CA, USA","institution_ids":["https://openalex.org/I1324840837"]},{"raw_affiliation_string":"[IPG, Hewlett-Packard Co., San Diego, CA]","institution_ids":["https://openalex.org/I1324840837"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017417100","display_name":"Johan Lim","orcid":"https://orcid.org/0000-0003-3130-9820"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Johan Lim","raw_affiliation_strings":["Department of Applied Statistics, Yonsei University, Seoul, South Korea","Dept. of Appl. Stat., Yonsei Univ., Seoul"],"affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Dept. of Appl. Stat., Yonsei Univ., Seoul","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110221608","display_name":"Robert. M. Gray","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert. M. Gray","raw_affiliation_strings":["Department of Electrical Engineering, University of Stanford, Stanford, CA, USA","Dept. of Electr. Eng., Stanford Univ., Stanford, CA#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Stanford, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Dept. of Electr. Eng., Stanford Univ., Stanford, CA#TAB#","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5054626081"],"corresponding_institution_ids":["https://openalex.org/I1324840837"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.13318972,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"2268","last_page":"2271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9973000288009644,"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":0.9966999888420105,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.692954957485199},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5772570371627808},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5421531796455383},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5373584032058716},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5162968039512634},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.484872430562973},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48003995418548584},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.47243958711624146},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45451685786247253},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4428466260433197},{"id":"https://openalex.org/keywords/standard-test-image","display_name":"Standard test image","score":0.43889135122299194},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.43020719289779663},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32926493883132935},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.31820911169052124},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29750722646713257},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12905877828598022}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.692954957485199},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5772570371627808},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5421531796455383},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5373584032058716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5162968039512634},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.484872430562973},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48003995418548584},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.47243958711624146},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45451685786247253},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4428466260433197},{"id":"https://openalex.org/C180462255","wikidata":"https://www.wikidata.org/wiki/Q3559736","display_name":"Standard test image","level":4,"score":0.43889135122299194},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.43020719289779663},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32926493883132935},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.31820911169052124},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29750722646713257},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12905877828598022}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2008.4712243","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2008.4712243","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 15th IEEE International Conference on Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5600000023841858,"display_name":"Reduced inequalities"},{"id":"https://metadata.un.org/sdg/16","score":0.4300000071525574,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1987719797","https://openalex.org/W1998172750","https://openalex.org/W2072514525","https://openalex.org/W2112213407","https://openalex.org/W2122374500","https://openalex.org/W2128428741","https://openalex.org/W2160732595","https://openalex.org/W3195084822","https://openalex.org/W6800094421"],"related_works":["https://openalex.org/W2357322570","https://openalex.org/W2997591215","https://openalex.org/W2788344745","https://openalex.org/W2227541280","https://openalex.org/W2062336688","https://openalex.org/W2886934452","https://openalex.org/W1489099099","https://openalex.org/W2024369332","https://openalex.org/W2130769947","https://openalex.org/W2116498084"],"abstract_inverted_index":{"Noise":[0],"is":[1,125,129,255],"ubiquitous":[2],"in":[3,13,49,65,103,111,163,227,258],"real":[4],"life":[5],"and":[6,10,177,193,196,210,215,234],"changes":[7],"image":[8,19,50,96],"acquisition":[9],"processing":[11,20],"characteristics":[12],"an":[14],"uncontrolled":[15,63],"manner.":[16],"Highly":[17],"sophisticated":[18],"algorithms":[21],"developed":[22],"for":[23,32,138,251],"clean":[24,252],"images":[25,140,149,176,190],"often":[26],"malfunction":[27],"when":[28],"they":[29,54],"are":[30,114],"used":[31],"noisy":[33,95,147],"images.":[34,67,84],"For":[35],"example,":[36],"hidden":[37],"Markov":[38],"Gauss":[39],"mixture":[40],"models":[41],"(HMGMM)":[42],"have":[43,55],"been":[44],"shown":[45],"to":[46,58,62,77,97,131,141,166,187],"perform":[47],"well":[48,247],"segmentation":[51,232],"applications,":[52],"but":[53],"also":[56],"proved":[57],"be":[59,142],"quite":[60],"sensitive":[61],"noise":[64,195],"test":[66,83,148,175],"To":[68,179],"resolve":[69],"this":[70,123],"difficulty,":[71],"we":[72,184,238],"propose":[73],"a":[74],"modified":[75],"procedure":[76,219,243],"adjust":[78],"covariance":[79,90,109,134,154,160],"matrix":[80,91,161],"estimates":[81,92,162],"of":[82,93,116,122,136,146,229],"We":[85,200],"shrink":[86,156],"(or":[87,157],"expand)":[88,158],"the":[89,94,104,108,112,117,133,159,164,168,181,205,211,241,249,256],"make":[98],"them":[99],"consistent":[100,143],"with":[101,191,197,204],"those":[102,115,145],"codebooks.":[105,178],"Note":[106],"that":[107,126,217,240],"matrices":[110,135,155],"codebooks":[113,137,165],"noiseless":[118,139],"image.":[119],"The":[120,152],"novelty":[121],"paper":[124],"our":[127,202,218],"method":[128,203,209,214],"equivalent":[130],"adjusting":[132],"wit":[144],"without":[150],"retraining.":[151],"adjusted":[153],"minimize":[167],"overall":[169],"minimum":[170],"discrimination":[171],"information":[172],"distortion":[173],"between":[174],"illustrate":[180],"proposed":[182],"procedure,":[183],"apply":[185],"it":[186],"segmenting":[188],"aerial":[189],"Salt":[192],"Pepper":[194],"Gaussian":[198],"noise.":[199],"compare":[201],"median":[206],"filter":[207],"restoration":[208],"blind":[212],"deconvolution":[213],"show":[216],"has":[220],"better":[221],"performance":[222],"than":[223],"these":[224],"image-restoration-based":[225],"techniques":[226],"terms":[228],"both":[230],"visual":[231],"results":[233],"error":[235],"rate.":[236],"Further,":[237],"find":[239],"suggested":[242],"performs":[244],"almost":[245],"as":[246,248],"HMGMM":[250],"images,":[253],"which":[254],"benchmark":[257],"comparison.":[259]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}