{"id":"https://openalex.org/W2405188473","doi":"https://doi.org/10.5220/0005234101770184","title":"The Gradient Product Transform for Symmetry Detection and Blood Vessel Extraction","display_name":"The Gradient Product Transform for Symmetry Detection and Blood Vessel Extraction","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2405188473","doi":"https://doi.org/10.5220/0005234101770184","mag":"2405188473"},"language":"en","primary_location":{"id":"doi:10.5220/0005234101770184","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005234101770184","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Computer Vision Theory and Applications","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0005234101770184","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016590766","display_name":"Christoph Dalitz","orcid":"https://orcid.org/0000-0002-7004-5584"},"institutions":[{"id":"https://openalex.org/I4210113269","display_name":"Hochschule Niederrhein","ror":"https://ror.org/027b9qx26","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210113269"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Christoph Dalitz","raw_affiliation_strings":["Niederrhein University of Applied Sciences, Germany"],"affiliations":[{"raw_affiliation_string":"Niederrhein University of Applied Sciences, Germany","institution_ids":["https://openalex.org/I4210113269"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044429674","display_name":"Regina Pohle-Fr\u00f6hlich","orcid":"https://orcid.org/0000-0002-4655-6851"},"institutions":[{"id":"https://openalex.org/I4210113269","display_name":"Hochschule Niederrhein","ror":"https://ror.org/027b9qx26","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210113269"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Regina Pohle-Fr\u00f6hlich","raw_affiliation_strings":["Niederrhein University of Applied Sciences, Germany"],"affiliations":[{"raw_affiliation_string":"Niederrhein University of Applied Sciences, Germany","institution_ids":["https://openalex.org/I4210113269"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004915666","display_name":"Fabian Schmitt","orcid":"https://orcid.org/0009-0000-1782-0935"},"institutions":[{"id":"https://openalex.org/I4210113269","display_name":"Hochschule Niederrhein","ror":"https://ror.org/027b9qx26","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210113269"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Schmitt","raw_affiliation_strings":["Niederrhein University of Applied Sciences, Germany"],"affiliations":[{"raw_affiliation_string":"Niederrhein University of Applied Sciences, Germany","institution_ids":["https://openalex.org/I4210113269"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083086888","display_name":"Manuel Jeltsch","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113269","display_name":"Hochschule Niederrhein","ror":"https://ror.org/027b9qx26","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210113269"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Manuel Jeltsch","raw_affiliation_strings":["Niederrhein University of Applied Sciences, Germany"],"affiliations":[{"raw_affiliation_string":"Niederrhein University of Applied Sciences, Germany","institution_ids":["https://openalex.org/I4210113269"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5016590766"],"corresponding_institution_ids":["https://openalex.org/I4210113269"],"apc_list":null,"apc_paid":null,"fwci":0.2635,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64980905,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"177","last_page":"184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9979000091552734,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9944999814033508,"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/symmetry","display_name":"Symmetry (geometry)","score":0.67396080493927},{"id":"https://openalex.org/keywords/rotational-symmetry","display_name":"Rotational symmetry","score":0.6542752385139465},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48973774909973145},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48892489075660706},{"id":"https://openalex.org/keywords/radon-transform","display_name":"Radon transform","score":0.46419039368629456},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.46101489663124084},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.4568132162094116},{"id":"https://openalex.org/keywords/scalar","display_name":"Scalar (mathematics)","score":0.4501290023326874},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44268637895584106},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43446317315101624},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42628800868988037},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4201262593269348},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32326996326446533},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.19461432099342346}],"concepts":[{"id":"https://openalex.org/C2779886137","wikidata":"https://www.wikidata.org/wiki/Q21030012","display_name":"Symmetry (geometry)","level":2,"score":0.67396080493927},{"id":"https://openalex.org/C33026886","wikidata":"https://www.wikidata.org/wiki/Q593950","display_name":"Rotational symmetry","level":2,"score":0.6542752385139465},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48973774909973145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48892489075660706},{"id":"https://openalex.org/C197231052","wikidata":"https://www.wikidata.org/wiki/Q979829","display_name":"Radon transform","level":2,"score":0.46419039368629456},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.46101489663124084},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.4568132162094116},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.4501290023326874},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44268637895584106},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43446317315101624},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42628800868988037},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4201262593269348},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32326996326446533},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.19461432099342346},{"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:10.5220/0005234101770184","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005234101770184","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Computer Vision Theory and Applications","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5220/0005234101770184","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005234101770184","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Computer Vision Theory and Applications","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W486821505","https://openalex.org/W1564419782","https://openalex.org/W1566328901","https://openalex.org/W1877724968","https://openalex.org/W1981078224","https://openalex.org/W2047424380","https://openalex.org/W2062552601","https://openalex.org/W2096320880","https://openalex.org/W2129534965","https://openalex.org/W2150769593","https://openalex.org/W2160594803","https://openalex.org/W2293455407","https://openalex.org/W2547099463"],"related_works":["https://openalex.org/W4290802965","https://openalex.org/W97789383","https://openalex.org/W4289406402","https://openalex.org/W2727156679","https://openalex.org/W3087516072","https://openalex.org/W2067997904","https://openalex.org/W2364071303","https://openalex.org/W1483053255","https://openalex.org/W2896097814","https://openalex.org/W20221657"],"abstract_inverted_index":{"The":[0],"gradient":[116],"product":[2,117],"transform":[56,73,87,118],"is":[4,42,49,64,74],"a":[5,15,45],"recently":[6],"proposed":[7],"image":[8,13,82],"filter":[9,135],"for":[10,34,112,126,136],"assigning":[11],"each":[12],"point":[14],"symmetry":[16,40,55,59,128],"score":[17],"based":[18],"on":[19],"scalar":[20],"products":[21],"of":[22,38,93],"gradients.":[23],"In":[24,105],"this":[25],"article,":[26],"we":[27,52],"show":[28],"that":[29,62],"the":[30,36,39,54,72,86,115,122,133],"originally":[31],"suggested":[32],"method":[33,48],"finding":[35],"radius":[37],"region":[41],"unreliable,":[43],"and":[44,71,98,130],"more":[46,65],"robust":[47,66],"presented.":[50],"Moreover,":[51],"extend":[53],"to":[57,69,76,88,121],"rectangular":[58],"regions":[60],"so":[61],"it":[63],"with":[67,79,95,109],"respect":[68],"skew,":[70],"generalised":[75],"also":[77],"work":[78],"three":[80],"dimensional":[81],"data.":[83],"We":[84],"apply":[85],"two":[89],"different":[90],"problems:":[91],"detection":[92],"objects":[94],"rotational":[96,127],"symmetry,":[97],"blood":[99,137],"vessel":[100,138],"extraction":[101],"from":[102],"medical":[103],"images.":[104],"an":[106],"experimental":[107],"comparison":[108],"other":[110],"solutions":[111],"these":[113],"problems,":[114],"performs":[119],"comparable":[120],"best":[123],"known":[124],"algorithm":[125],"detection,":[129],"better":[131],"than":[132],"vesselness":[134],"extraction.":[139]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}