{"id":"https://openalex.org/W2171709750","doi":"https://doi.org/10.1109/icsmc.2009.5346779","title":"A non-reference measure for objective edge map evaluation","display_name":"A non-reference measure for objective edge map evaluation","publication_year":2009,"publication_date":"2009-10-01","ids":{"openalex":"https://openalex.org/W2171709750","doi":"https://doi.org/10.1109/icsmc.2009.5346779","mag":"2171709750"},"language":"en","primary_location":{"id":"doi:10.1109/icsmc.2009.5346779","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2009.5346779","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Systems, Man and Cybernetics","raw_type":"proceedings-article"},"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/A5022048399","display_name":"Shahan Nercessian","orcid":"https://orcid.org/0000-0003-4879-7365"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shahan Nercessian","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069074343","display_name":"Karen Panetta","orcid":"https://orcid.org/0000-0001-9435-4536"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karen Panetta","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048285657","display_name":"Sos \u0421. Agaian","orcid":"https://orcid.org/0000-0003-4601-4507"},"institutions":[{"id":"https://openalex.org/I45438204","display_name":"The University of Texas at San Antonio","ror":"https://ror.org/01kd65564","country_code":"US","type":"education","lineage":["https://openalex.org/I45438204"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sos Agaian","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Texas, San Antonio, San Antonio, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Texas, San Antonio, San Antonio, TX, USA","institution_ids":["https://openalex.org/I45438204"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4563","last_page":"4568"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","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/T10052","display_name":"Medical Image Segmentation Techniques","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9976000189781189,"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/measure","display_name":"Measure (data warehouse)","score":0.8311015367507935},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.7855716943740845},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.7670084834098816},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.7190974354743958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7030591368675232},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6454017162322998},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6304200887680054},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5617064833641052},{"id":"https://openalex.org/keywords/canny-edge-detector","display_name":"Canny edge detector","score":0.5035590529441833},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4157443642616272},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3851090669631958},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34922969341278076},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2779123783111572}],"concepts":[{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.8311015367507935},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.7855716943740845},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7670084834098816},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.7190974354743958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7030591368675232},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6454017162322998},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6304200887680054},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5617064833641052},{"id":"https://openalex.org/C14705441","wikidata":"https://www.wikidata.org/wiki/Q597183","display_name":"Canny edge detector","level":5,"score":0.5035590529441833},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4157443642616272},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3851090669631958},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34922969341278076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2779123783111572},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsmc.2009.5346779","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2009.5346779","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Systems, Man and Cybernetics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1575739010","https://openalex.org/W1580389772","https://openalex.org/W1966861145","https://openalex.org/W1968350323","https://openalex.org/W2088615398","https://openalex.org/W2100415658","https://openalex.org/W2133477468","https://openalex.org/W2138781866","https://openalex.org/W2150564835","https://openalex.org/W2152474030","https://openalex.org/W2153394113","https://openalex.org/W2156233337","https://openalex.org/W2295936755","https://openalex.org/W2626610348","https://openalex.org/W4251740737"],"related_works":["https://openalex.org/W3169126738","https://openalex.org/W1994279415","https://openalex.org/W2558559991","https://openalex.org/W1986338341","https://openalex.org/W2980082320","https://openalex.org/W2545065926","https://openalex.org/W3093839383","https://openalex.org/W2387510934","https://openalex.org/W2128085731","https://openalex.org/W4293054829"],"abstract_inverted_index":{"Edge":[0],"detection":[1],"has":[2],"been":[3],"used":[4],"extensively":[5],"as":[6,67,69,137,147],"a":[7,52,95,118,138,148,159],"preprocessing":[8],"step":[9],"for":[10,98,122,158],"many":[11],"computer":[12],"vision":[13],"tasks.":[14],"Due":[15],"to":[16,39,49,54,70,105],"its":[17],"importance":[18],"in":[19,103],"image":[20,90],"processing":[21],"and":[22,30,146],"the":[23,60,82,107,110,115,130,133,152],"highly":[24],"subjective":[25],"nature":[26],"of":[27,33,57,73,109,117,132,140,150,155],"human":[28],"evaluation":[29,44],"visual":[31],"comparison":[32],"edge":[34,42,62,77,124,143,156],"detectors,":[35],"it":[36],"is":[37,101],"desirable":[38],"formulate":[40],"objective":[41,123],"map":[43,125],"measures.":[45],"One":[46],"would":[47],"like":[48],"use":[50],"such":[51],"measure":[53,121,135],"make":[55,71],"comparisons":[56,72],"results":[58,74,128],"using":[59,75],"same":[61],"detector":[63,144],"with":[64],"different":[65,76],"parameters":[66,145],"well":[68],"detectors.":[78],"Reconstruction-based":[79],"measures":[80,100],"have":[81],"clear":[83],"advantage":[84],"that":[85],"they":[86],"effectively":[87],"incorporate":[88],"original":[89],"data.":[91],"In":[92],"this":[93],"paper,":[94],"general":[96],"model":[97],"reconstruction-based":[99,111],"established":[102],"order":[104],"alleviate":[106],"shortcomings":[108],"measures,":[112],"followed":[113],"by":[114],"formulation":[116],"new":[119,134],"non-reference":[120],"evaluation.":[126],"Experimental":[127],"illustrate":[129],"effectiveness":[131],"both":[136],"means":[139,149],"selecting":[141],"optimal":[142],"determining":[151],"relative":[153],"performance":[154],"detectors":[157],"given":[160],"image.":[161]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}