{"id":"https://openalex.org/W2121469718","doi":"https://doi.org/10.1109/btas.2012.6374563","title":"Efficient iris segmentation using Grow-Cut algorithm for remotely acquired iris images","display_name":"Efficient iris segmentation using Grow-Cut algorithm for remotely acquired iris images","publication_year":2012,"publication_date":"2012-09-01","ids":{"openalex":"https://openalex.org/W2121469718","doi":"https://doi.org/10.1109/btas.2012.6374563","mag":"2121469718"},"language":"en","primary_location":{"id":"doi:10.1109/btas.2012.6374563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/btas.2012.6374563","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)","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/A5065224542","display_name":"Chun-Wei Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Chun-Wei Tan","raw_affiliation_strings":["Department of Computing, Hong Kong Polytechnic University, Hong Kong, China","Department of Computing, The Hong Kong Polytechnic, University, Hung Hom, Kowloon, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"Department of Computing, The Hong Kong Polytechnic, University, Hung Hom, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051203367","display_name":"Ajay Kumar","orcid":"https://orcid.org/0000-0002-3761-2436"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Ajay Kumar","raw_affiliation_strings":["Department of Computing, Hong Kong Polytechnic University, Hong Kong, China","Department of Computing, The Hong Kong Polytechnic, University, Hung Hom, Kowloon, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"Department of Computing, The Hong Kong Polytechnic, University, Hung Hom, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5065224542"],"corresponding_institution_ids":["https://openalex.org/I14243506"],"apc_list":null,"apc_paid":null,"fwci":1.2242,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.83363332,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"6005","issue":null,"first_page":"99","last_page":"104"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T12029","display_name":"DNA and Biological Computing","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12162","display_name":"Cellular Automata and Applications","score":0.9638000130653381,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/segmentation","display_name":"Segmentation","score":0.8771448731422424},{"id":"https://openalex.org/keywords/iris","display_name":"IRIS (biosensor)","score":0.847659707069397},{"id":"https://openalex.org/keywords/iris-recognition","display_name":"Iris recognition","score":0.7459501624107361},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7320635318756104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7217975854873657},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6153842806816101},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.5815491080284119},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5597067475318909},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4990675449371338},{"id":"https://openalex.org/keywords/simplicity","display_name":"Simplicity","score":0.4145601987838745},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.41350236535072327},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.21706268191337585}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8771448731422424},{"id":"https://openalex.org/C2779503344","wikidata":"https://www.wikidata.org/wiki/Q5973514","display_name":"IRIS (biosensor)","level":3,"score":0.847659707069397},{"id":"https://openalex.org/C112356035","wikidata":"https://www.wikidata.org/wiki/Q1672722","display_name":"Iris recognition","level":3,"score":0.7459501624107361},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7320635318756104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7217975854873657},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6153842806816101},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.5815491080284119},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5597067475318909},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4990675449371338},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.4145601987838745},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.41350236535072327},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.21706268191337585},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"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},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/btas.2012.6374563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/btas.2012.6374563","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)","raw_type":"proceedings-article"},{"id":"pmh:oai:ira.lib.polyu.edu.hk:10397/33994","is_oa":false,"landing_page_url":"http://hdl.handle.net/10397/33994","pdf_url":null,"source":{"id":"https://openalex.org/S4306400205","display_name":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I14243506","host_organization_name":"Hong Kong Polytechnic University","host_organization_lineage":["https://openalex.org/I14243506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W155959724","https://openalex.org/W157158741","https://openalex.org/W1569369988","https://openalex.org/W1974821667","https://openalex.org/W1993611818","https://openalex.org/W2006699888","https://openalex.org/W2025546194","https://openalex.org/W2030958619","https://openalex.org/W2037610579","https://openalex.org/W2046535006","https://openalex.org/W2072188593","https://openalex.org/W2115718877","https://openalex.org/W2118946293","https://openalex.org/W2127011353","https://openalex.org/W2137659841","https://openalex.org/W2139695910","https://openalex.org/W2155138846","https://openalex.org/W2164598857","https://openalex.org/W2167075312","https://openalex.org/W2168567026","https://openalex.org/W2169096120","https://openalex.org/W2171759622","https://openalex.org/W2172128630","https://openalex.org/W3210232381","https://openalex.org/W6606375356","https://openalex.org/W6606412717","https://openalex.org/W6803376173"],"related_works":["https://openalex.org/W2162640687","https://openalex.org/W2759939383","https://openalex.org/W2355560018","https://openalex.org/W2147209541","https://openalex.org/W4231710054","https://openalex.org/W2952386695","https://openalex.org/W2557390811","https://openalex.org/W3133795085","https://openalex.org/W3213945064","https://openalex.org/W2784078054"],"abstract_inverted_index":{"This":[0],"paper":[1,111],"presents":[2],"a":[3],"computationally":[4],"efficient":[5],"iris":[6,11,24,56,64,119,137],"segmentation":[7,25,57,65,95,120,131],"approach":[8,26,46],"for":[9,60,133],"segmenting":[10],"images":[12],"acquired":[13,136],"from":[14,71],"at-a-distance":[15],"and":[16,80,90],"under":[17],"less":[18],"constrained":[19],"imaging":[20],"conditions.":[21],"The":[22,40,67,105],"proposed":[23,102],"is":[27,47],"developed":[28,45,59,118],"based":[29],"on":[30],"the":[31,37,44,54,61,72,93,100,114,117,134],"cellular":[32],"automata":[33],"which":[34],"evolves":[35],"using":[36],"Grow-Cut":[38],"algorithm.":[39],"major":[41],"advantage":[42],"of":[43,87,116],"its":[48],"computational":[49,126],"simplicity":[50],"as":[51,97],"compared":[52,98],"to":[53,99],"prior":[55],"approaches":[58],"visible":[62],"illumination":[63],"images.":[66,138],"experimental":[68,106],"results":[69,107],"obtained":[70],"three":[73],"publicly":[74],"available":[75],"databases,":[76],"i.e.":[77],"UBIRIS.v2,":[78],"FRGC":[79],"CASIA.v4-distance":[81],"have":[82],"respectively":[83],"achieved":[84],"average":[85,94],"improvement":[86],"34.8%,":[88],"31.5%":[89],"31.4%":[91],"in":[92,109,125],"error,":[96],"recently":[101],"competing/best":[103],"approaches.":[104],"presented":[108],"this":[110],"clearly":[112],"demonstrate":[113],"superiority":[115],"approach,":[121],"i.e.,":[122],"significant":[123],"reduction":[124],"complexity":[127],"while":[128],"providing":[129],"comparable":[130],"performance,":[132],"distantly":[135]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
