{"id":"https://openalex.org/W2060624075","doi":"https://doi.org/10.1117/12.2030808","title":"Efficient method of image edge detection based on FSVM","display_name":"Efficient method of image edge detection based on FSVM","publication_year":2013,"publication_date":"2013-07-19","ids":{"openalex":"https://openalex.org/W2060624075","doi":"https://doi.org/10.1117/12.2030808","mag":"2060624075"},"language":"en","primary_location":{"id":"doi:10.1117/12.2030808","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2030808","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5076997325","display_name":"Aiping Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I3131412887","display_name":"Jiangxi University of Technology","ror":"https://ror.org/05k2j8e48","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131412887"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Aiping Cai","raw_affiliation_strings":["Jiangxi Univ. of Technology (China)"],"affiliations":[{"raw_affiliation_string":"Jiangxi Univ. of Technology (China)","institution_ids":["https://openalex.org/I3131412887"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035583106","display_name":"Xiaomei Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I3131412887","display_name":"Jiangxi University of Technology","ror":"https://ror.org/05k2j8e48","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131412887"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaomei Xiong","raw_affiliation_strings":["Jiangxi Univ. of Technology (China)"],"affiliations":[{"raw_affiliation_string":"Jiangxi Univ. of Technology (China)","institution_ids":["https://openalex.org/I3131412887"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076997325"],"corresponding_institution_ids":["https://openalex.org/I3131412887"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13608182,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8878","issue":null,"first_page":"887830","last_page":"887830"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13717","display_name":"Advanced Algorithms and Applications","score":0.9408000111579895,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.9408000111579895,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9322999715805054,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12549","display_name":"Image and Object Detection Techniques","score":0.9248999953269958,"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/computer-science","display_name":"Computer science","score":0.8160740733146667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7242776155471802},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.6666513085365295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6566789150238037},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6533628106117249},{"id":"https://openalex.org/keywords/canny-edge-detector","display_name":"Canny edge detector","score":0.6432927250862122},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6285216808319092},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5484285950660706},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5474153757095337},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47972384095191956},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43511682748794556},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4286540150642395},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41600310802459717},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.32711514830589294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8160740733146667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7242776155471802},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.6666513085365295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6566789150238037},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6533628106117249},{"id":"https://openalex.org/C14705441","wikidata":"https://www.wikidata.org/wiki/Q597183","display_name":"Canny edge detector","level":5,"score":0.6432927250862122},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6285216808319092},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5484285950660706},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5474153757095337},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47972384095191956},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43511682748794556},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4286540150642395},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41600310802459717},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.32711514830589294},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2030808","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2030808","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3169126738","https://openalex.org/W1994279415","https://openalex.org/W2558559991","https://openalex.org/W1986338341","https://openalex.org/W2545065926","https://openalex.org/W2980082320","https://openalex.org/W3093839383","https://openalex.org/W2387510934","https://openalex.org/W2015446345","https://openalex.org/W3132644099"],"abstract_inverted_index":{"For":[0],"efficient":[1],"object":[2,99],"cover":[3],"edge":[4,22,38,110],"detection":[5,23,44,111],"in":[6],"digital":[7],"images,":[8],"this":[9,123],"paper":[10],"studied":[11],"traditional":[12],"methods":[13],"and":[14,28,57,80,89,116],"algorithm":[15,24,45],"based":[16,46],"on":[17,47],"SVM.":[18],"It":[19],"analyzed":[20],"Canny":[21],"existed":[25],"some":[26,77],"pseudo-edge":[27],"poor":[29],"anti-noise":[30],"capability.":[31],"In":[32],"order":[33],"to":[34,63],"provide":[35],"a":[36,42,67],"reliable":[37],"extraction":[39],"method,":[40],"propose":[41],"new":[43,68,83],"FSVM.":[48],"Which":[49],"contains":[50],"several":[51],"steps:":[52],"first,":[53],"trains":[54],"classify":[55],"sample":[56,70],"gives":[58],"the":[59,75,82,93,98,103],"different":[60,64],"membership":[61],"function":[62],"samples.":[65],"Then,":[66],"training":[69],"is":[71],"formed":[72],"by":[73,101],"increase":[74],"punishment":[76],"wrong":[78],"sub-sample,":[79],"use":[81],"FSVM":[84],"classification":[85],"model":[86],"for":[87],"train":[88],"test":[90],"them.":[91],"Finally":[92],"edges":[94],"are":[95],"extracted":[96],"of":[97],"image":[100,112],"using":[102],"model.":[104],"Experimental":[105],"result":[106],"shows":[107],"that":[108,122],"good":[109,126],"will":[113],"be":[114],"obtained":[115],"adding":[117],"noise":[118],"experiments":[119],"results":[120],"show":[121],"method":[124],"has":[125],"anti-noise.":[127]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}