{"id":"https://openalex.org/W2179193716","doi":"https://doi.org/10.1109/ipta.2015.7367128","title":"Shape matching using keypoints extracted from both the foreground and the background of binary images","display_name":"Shape matching using keypoints extracted from both the foreground and the background of binary images","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2179193716","doi":"https://doi.org/10.1109/ipta.2015.7367128","mag":"2179193716"},"language":"en","primary_location":{"id":"doi:10.1109/ipta.2015.7367128","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2015.7367128","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)","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/A5076218326","display_name":"Houssem Chatbri","orcid":null},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Houssem Chatbri","raw_affiliation_strings":["Department of Computer Science, University of Tsukuba, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025444128","display_name":"Kenny Davila","orcid":"https://orcid.org/0000-0001-6308-7113"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kenny Davila","raw_affiliation_strings":["Department of Computer Science Rochester, Institute of Technology, Rochester, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Rochester, Institute of Technology, Rochester, NY, USA","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078481609","display_name":"Keisuke Kameyama","orcid":"https://orcid.org/0000-0001-8810-8646"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keisuke Kameyama","raw_affiliation_strings":["Tsukuba Daigaku, Tsukuba, Ibaraki, JP"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsukuba Daigaku, Tsukuba, Ibaraki, JP","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084564610","display_name":"Richard Zanibbi","orcid":"https://orcid.org/0000-0001-5921-9750"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard Zanibbi","raw_affiliation_strings":["Department of Computer Science Rochester, Institute of Technology, Rochester, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Rochester, Institute of Technology, Rochester, NY, USA","institution_ids":["https://openalex.org/I155173764"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5616,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75357682,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"205","last_page":"210"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9998999834060669,"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":0.9959999918937683,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8022133111953735},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6536774039268494},{"id":"https://openalex.org/keywords/silhouette","display_name":"Silhouette","score":0.6473942399024963},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6405177116394043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.579862117767334},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5733191967010498},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5356746315956116},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5251243710517883},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4870493710041046},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4626491963863373},{"id":"https://openalex.org/keywords/binary-image","display_name":"Binary image","score":0.4610866904258728},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4504167437553406},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44035786390304565},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3907148838043213},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.19710299372673035}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8022133111953735},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6536774039268494},{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.6473942399024963},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6405177116394043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.579862117767334},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5733191967010498},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5356746315956116},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5251243710517883},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4870493710041046},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4626491963863373},{"id":"https://openalex.org/C193828747","wikidata":"https://www.wikidata.org/wiki/Q864118","display_name":"Binary image","level":4,"score":0.4610866904258728},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4504167437553406},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44035786390304565},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3907148838043213},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.19710299372673035},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","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:10.1109/ipta.2015.7367128","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2015.7367128","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W13151587","https://openalex.org/W303300579","https://openalex.org/W1499655453","https://openalex.org/W1541960437","https://openalex.org/W1570052007","https://openalex.org/W2000010444","https://openalex.org/W2012515212","https://openalex.org/W2020479533","https://openalex.org/W2049823565","https://openalex.org/W2057175746","https://openalex.org/W2058984722","https://openalex.org/W2080850829","https://openalex.org/W2088895522","https://openalex.org/W2094816543","https://openalex.org/W2108556791","https://openalex.org/W2108611942","https://openalex.org/W2111569598","https://openalex.org/W2116112220","https://openalex.org/W2130932233","https://openalex.org/W2133059825","https://openalex.org/W2133155955","https://openalex.org/W2151103935","https://openalex.org/W2166257042","https://openalex.org/W2474211219","https://openalex.org/W2501347831","https://openalex.org/W4246482510","https://openalex.org/W6670435977","https://openalex.org/W6676245372","https://openalex.org/W6680096826","https://openalex.org/W6720907354","https://openalex.org/W6996882303"],"related_works":["https://openalex.org/W1622964048","https://openalex.org/W30315714","https://openalex.org/W1965274140","https://openalex.org/W779885325","https://openalex.org/W2150972844","https://openalex.org/W2393615320","https://openalex.org/W1989545120","https://openalex.org/W2164349598","https://openalex.org/W2161674153","https://openalex.org/W1978863158"],"abstract_inverted_index":{"We":[0,100],"introduce":[1],"a":[2,88],"descriptor":[3,103,119],"for":[4,84],"shape":[5],"feature":[6],"extraction":[7],"and":[8,19,76,112,126],"matching":[9,140],"using":[10,87,104],"keypoints":[11,69,137],"that":[12,117,127],"are":[13,47,78,82],"extracted":[14,83],"from":[15],"both":[16],"the":[17,20,32,44,65,97,134],"foreground":[18],"background":[21],"of":[22,41,53,55,68,73,106,136],"binary":[23],"images.":[24],"First,":[25],"distance":[26],"transform":[27],"(DT)":[28],"is":[29,61,93,120,130],"applied":[30,62],"on":[31,96],"image":[33,74],"after":[34],"contour":[35],"detection.":[36],"Then,":[37],"connected":[38],"components":[39],"(CCs)":[40],"pixels":[42],"having":[43],"same":[45],"intensity":[46],"extracted.":[48],"Keypoints":[49],"correspond":[50],"to":[51],"centers":[52],"mass":[54],"CCs.":[56],"A":[57],"keypoint":[58,86,128],"filtering":[59,129],"mechanism":[60],"by":[63],"estimating":[64],"spatial":[66],"stability":[67],"when":[70],"successive":[71],"iterations":[72],"blurring":[75],"binarization":[77],"applied.":[79],"Finally,":[80],"features":[81],"each":[85],"round":[89],"layout":[90],"which":[91],"radius":[92],"set":[94],"depending":[95],"keypoint's":[98],"location.":[99],"evaluate":[101],"our":[102,118],"datasets":[105],"silhouette":[107],"images,":[108],"handwritten":[109],"math":[110],"expressions,":[111],"logos.":[113],"Experimental":[114],"results":[115],"show":[116],"competitive":[121],"compared":[122],"with":[123],"state-of-the-art":[124],"methods,":[125],"effective":[131],"in":[132],"reducing":[133],"number":[135],"without":[138],"compromising":[139],"performances.":[141]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}