{"id":"https://openalex.org/W2024920543","doi":"https://doi.org/10.1109/cvprw.2013.63","title":"Shadow Segmentation in SAS and SAR Using Bayesian Elastic Contours","display_name":"Shadow Segmentation in SAS and SAR Using Bayesian Elastic Contours","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2024920543","doi":"https://doi.org/10.1109/cvprw.2013.63","mag":"2024920543"},"language":"en","primary_location":{"id":"doi:10.1109/cvprw.2013.63","doi":"https://doi.org/10.1109/cvprw.2013.63","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw.2013.63","pdf_url":null,"source":{"id":"https://openalex.org/S4363607748","display_name":"IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5085463907","display_name":"Darshan Bryner","orcid":"https://orcid.org/0000-0003-4987-9274","observed_orcids":["https://orcid.org/0000-0003-4987-9274"]},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Darshan Bryner","raw_affiliation_strings":["Department of Statistics, Florida State University, Tallahassee, FL, USA","Dept. of Stat., Florida State Univ., Tallahassee, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]},{"raw_affiliation_string":"Dept. of Stat., Florida State Univ., Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086786635","display_name":"Anuj Srivastava","orcid":"https://orcid.org/0000-0001-7406-0338","observed_orcids":["https://orcid.org/0000-0001-7406-0338"]},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anuj Srivastava","raw_affiliation_strings":["Department of Statistics, Florida State University, Tallahassee, FL, USA","Dept. of Stat., Florida State Univ., Tallahassee, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]},{"raw_affiliation_string":"Dept. of Stat., Florida State Univ., Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I103163165"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12159329,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"87","issue":null,"first_page":"375","last_page":"380"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.6190999746322632,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.6190999746322632,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.2761000096797943,"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/T11698","display_name":"Underwater Acoustics Research","score":0.029600000008940697,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/shadow-segmentation","display_name":"shadow segmentation","score":0.9409999847412109},{"id":"https://openalex.org/keywords/synthetic-aperture-sonar","display_name":"synthetic aperture sonar","score":0.8460000157356262},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"synthetic aperture radar","score":1.0},{"id":"https://openalex.org/keywords/snakes","display_name":"snakes","score":0.8740000128746033},{"id":"https://openalex.org/keywords/boundary-extraction","display_name":"boundary extraction","score":0.6980000138282776},{"id":"https://openalex.org/keywords/shape-analysis","display_name":"shape analysis","score":0.5270000100135803},{"id":"https://openalex.org/keywords/variational-segmentation","display_name":"variational segmentation","score":0.41600000858306885}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7829452753067017},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.717897891998291},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.6568496227264404},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6002854108810425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.60008305311203},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5898231863975525},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.5551719069480896},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.520327091217041},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5102429389953613},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49642854928970337},{"id":"https://openalex.org/C181255713","wikidata":"https://www.wikidata.org/wiki/Q7662740","display_name":"Synthetic aperture sonar","level":3,"score":0.4849150776863098},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4261690676212311},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.42168065905570984},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.4111379086971283},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39810752868652344},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2884131073951721},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.19290316104888916},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.15796443819999695},{"id":"https://openalex.org/C97686452","wikidata":"https://www.wikidata.org/wiki/Q7604153","display_name":"Static analysis","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvprw.2013.63","doi":"https://doi.org/10.1109/cvprw.2013.63","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw.2013.63","pdf_url":null,"source":{"id":"https://openalex.org/S4363607748","display_name":"IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1543363024","https://openalex.org/W2008802909","https://openalex.org/W2042204212","https://openalex.org/W2093258721","https://openalex.org/W2104095591","https://openalex.org/W2115087759","https://openalex.org/W2128012355","https://openalex.org/W2146932984","https://openalex.org/W2159438535","https://openalex.org/W2165168736","https://openalex.org/W2167338900","https://openalex.org/W4252097634"],"related_works":["https://openalex.org/W2080852750","https://openalex.org/W4255940245","https://openalex.org/W2147427282","https://openalex.org/W2005451401","https://openalex.org/W3145569374","https://openalex.org/W7095571522","https://openalex.org/W1491051173","https://openalex.org/W2355151389","https://openalex.org/W2085845644","https://openalex.org/W2022346954"],"abstract_inverted_index":{"We":[0,145],"present":[1],"a":[2,151,155],"variational":[3],"framework":[4,22],"for":[5,16,26],"naturally":[6],"incorporating":[7],"prior":[8,130],"shape":[9,129,134,165,169,174],"knowledge":[10],"in":[11,19,138,160],"guidance":[12],"of":[13,46,58,101],"active":[14,142],"contours":[15],"boundary":[17,34],"extraction":[18,100],"images.":[20,68],"This":[21],"is":[23,36],"especially":[24],"suitable":[25],"images":[27],"collected":[28],"outside":[29],"the":[30,56,87,90,99,133,141],"visible":[31],"spectrum,":[32],"where":[33],"estimation":[35],"difficult":[37],"due":[38],"to":[39,86,140],"low":[40,42],"contrast,":[41],"resolution,":[43],"and":[44,48,63,94,121,154,171],"presence":[45],"noise":[47],"clutter.":[49],"Accordingly,":[50],"we":[51,96,124],"illustrate":[52],"this":[53],"approach":[54],"using":[55],"segmentation":[57,162],"synthetic":[59,64],"aperture":[60,65],"sonar":[61],"(SAS)":[62],"radar":[66],"(SAR)":[67],"The":[69],"shadows":[70],"produced":[71],"from":[72],"these":[73],"imaging":[74],"modalities":[75],"often":[76],"times":[77],"offer":[78],"more":[79],"consistent":[80],"pixel":[81],"values":[82],"with":[83,117,167,172],"clearer":[84],"contrast":[85],"background":[88],"than":[89,105],"targets":[91],"pixels":[92],"themselves,":[93],"thus":[95],"focus":[97],"on":[98,132,149],"shadow":[102,109],"boundaries":[103],"rather":[104],"target":[106,119],"boundaries.":[107],"Since":[108],"shapes":[110],"can":[111],"vary":[112],"under":[113],"approximately":[114],"affine":[115],"transformation":[116],"different":[118],"range":[120],"aspect":[122],"angle,":[123],"incorporate":[125],"an":[126],"affine-invariant,":[127],"elastic":[128],"based":[131],"analysis":[135],"techniques":[136],"developed":[137],"[2]":[139],"contour":[143],"model.":[144],"show":[146],"experimental":[147],"results":[148],"both":[150],"simulated":[152,156],"SAS":[153],"SAR":[157],"image":[158],"database":[159],"three":[161],"scenarios:":[163],"without":[164],"prior,":[166,170],"similarity-invariant":[168],"affine-invariant":[173],"prior.":[175]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2016-06-24T00:00:00"}