{"id":"https://openalex.org/W2025589589","doi":"https://doi.org/10.1002/ima.1850030308","title":"Low\u2010level bayesian segmentation of piecewise\u2010homogeneous noisy and textured images","display_name":"Low\u2010level bayesian segmentation of piecewise\u2010homogeneous noisy and textured images","publication_year":1991,"publication_date":"1991-09-01","ids":{"openalex":"https://openalex.org/W2025589589","doi":"https://doi.org/10.1002/ima.1850030308","mag":"2025589589"},"language":"en","primary_location":{"id":"doi:10.1002/ima.1850030308","is_oa":false,"landing_page_url":"https://doi.org/10.1002/ima.1850030308","pdf_url":null,"source":{"id":"https://openalex.org/S15952048","display_name":"International Journal of Imaging Systems and Technology","issn_l":"0899-9457","issn":["0899-9457","1098-1098"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Imaging Systems and Technology","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/A5114377140","display_name":"Georgy Gimel\u2019farb","orcid":"https://orcid.org/0000-0003-2120-9391"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"G. L. Gimel'farb","raw_affiliation_strings":["Department 420, V. M. Glushkov Institute of Cybernetics, Academy of Sciences of the USSR, Prospekt Akademika Glushkova, 20/22, Kiev 207, 252207, USSR"],"affiliations":[{"raw_affiliation_string":"Department 420, V. M. Glushkov Institute of Cybernetics, Academy of Sciences of the USSR, Prospekt Akademika Glushkova, 20/22, Kiev 207, 252207, USSR","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110908982","display_name":"A. Zalesny","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"A. V. Zalesny","raw_affiliation_strings":["Department 420, V. M. Glushkov Institute of Cybernetics, Academy of Sciences of the USSR, Prospekt Akademika Glushkova, 20/22, Kiev 207, 252207, USSR"],"affiliations":[{"raw_affiliation_string":"Department 420, V. M. Glushkov Institute of Cybernetics, Academy of Sciences of the USSR, Prospekt Akademika Glushkova, 20/22, Kiev 207, 252207, USSR","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5114377140"],"corresponding_institution_ids":[],"apc_list":{"value":3450,"currency":"USD","value_usd":3450},"apc_paid":null,"fwci":4.1882,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.92874396,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":"3","first_page":"227","last_page":"243"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9861000180244446,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9854000210762024,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.7514469623565674},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.6914912462234497},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6255053281784058},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6107128858566284},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5461531281471252},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5121397972106934},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4758717119693756},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4665166139602661},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4588629901409149},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4474315345287323},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.430624783039093},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.4116487205028534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4016962945461273},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.38145560026168823},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3589925169944763},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.35750094056129456},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35378479957580566},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23173969984054565},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17561578750610352},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.1356126070022583},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.09140178561210632}],"concepts":[{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.7514469623565674},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.6914912462234497},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6255053281784058},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6107128858566284},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5461531281471252},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5121397972106934},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4758717119693756},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4665166139602661},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4588629901409149},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4474315345287323},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.430624783039093},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.4116487205028534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4016962945461273},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.38145560026168823},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3589925169944763},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35750094056129456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35378479957580566},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23173969984054565},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17561578750610352},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.1356126070022583},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.09140178561210632},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/ima.1850030308","is_oa":false,"landing_page_url":"https://doi.org/10.1002/ima.1850030308","pdf_url":null,"source":{"id":"https://openalex.org/S15952048","display_name":"International Journal of Imaging Systems and Technology","issn_l":"0899-9457","issn":["0899-9457","1098-1098"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Imaging Systems and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6899999976158142,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1989222601","https://openalex.org/W2002132954","https://openalex.org/W2017656802","https://openalex.org/W2020999234","https://openalex.org/W2024060531","https://openalex.org/W2032785035","https://openalex.org/W2056760934","https://openalex.org/W2063225631","https://openalex.org/W2065301447","https://openalex.org/W2065448588","https://openalex.org/W2074166361","https://openalex.org/W2088059995","https://openalex.org/W2093881646","https://openalex.org/W2114220616","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2072169887","https://openalex.org/W4386272753","https://openalex.org/W1839961359","https://openalex.org/W2075146114","https://openalex.org/W2097090565","https://openalex.org/W2008444830","https://openalex.org/W2100805585","https://openalex.org/W2964314781","https://openalex.org/W3006565005","https://openalex.org/W2804375118"],"abstract_inverted_index":{"Abstract":[0],"We":[1],"present":[2],"a":[3,29,67,106,120,156],"novel":[4],"approach":[5],"to":[6,108,158],"image":[7],"segmentation,":[8],"differing":[9],"from":[10],"the":[11,17,20,40,53,57,63,70,73,77,85,88,91,95,100,110,117,124,132,139,147,151,164],"known":[12],"\u201csimulated":[13],"annealing\u201d":[14],"method":[15],"in":[16,38,129],"following":[18],"ways:":[19],"compound":[21],"Bayesian":[22],"decision":[23],"rule":[24],"and":[25,59,83,115],"consequent":[26],"maximal":[27,160],"marginal":[28,119],"posteriori":[30,121],"probability":[31,102,153],"(MMAP)":[32],"estimates":[33,162],"of":[34,56,72,76,84,99,112,123,134,146,150,163],"desired":[35],"region":[36,60,74,125],"labels":[37,126,135],"pixels;":[39],"two\u2010":[41],"or":[42],"three\u2010level":[43],"piecewise\u2010homogeneous":[44],"Gibbs":[45,101,152],"random":[46],"field":[47,114],"with":[48,94,143],"constant":[49,96],"control":[50,97,148],"parameters":[51,98,149],"as":[52,105,155],"probabilistic":[54],"model":[55,68],"images":[58],"maps":[61],"(in":[62],"general":[64],"case":[65],"such":[66],"integrates":[69],"submodels":[71],"map,":[75],"ideal":[78,89],"intensities":[79],"within":[80],"each":[81,130],"region,":[82],"noise":[86],"distorting":[87],"intensities);":[90],"stochastic":[92,141],"relaxation":[93,142],"distribution":[103,154],"only":[104],"tool":[107,157],"obtain":[109],"samples":[111],"this":[113],"estimate":[116],"unknown":[118,165],"probabilities":[122],"by":[127],"collecting":[128],"pixel":[131],"histogram":[133],"for":[136],"these":[137,166],"samples;":[138],"like":[140],"directed":[144],"variation":[145],"find":[159],"likelihood":[161],"parameters.":[167],"Some":[168],"experimental":[169],"results":[170],"are":[171],"presented.":[172]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
