{"id":"https://openalex.org/W2903126140","doi":"https://doi.org/10.1109/icpr.2018.8545613","title":"Segmentation Edit Distance","display_name":"Segmentation Edit Distance","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903126140","doi":"https://doi.org/10.1109/icpr.2018.8545613","mag":"2903126140"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5063924795","display_name":"Daniel Pucher","orcid":null},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Daniel Pucher","raw_affiliation_strings":["TU Wien, Pattern Recognition and Image Processing Group (PRIP), Austria"],"affiliations":[{"raw_affiliation_string":"TU Wien, Pattern Recognition and Image Processing Group (PRIP), Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081777844","display_name":"Walter G. Kropatsch","orcid":"https://orcid.org/0000-0003-4915-4118"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Walter G. Kropatsch","raw_affiliation_strings":["TU Wien, Pattern Recognition and Image Processing Group (PRIP), Austria"],"affiliations":[{"raw_affiliation_string":"TU Wien, Pattern Recognition and Image Processing Group (PRIP), Austria","institution_ids":["https://openalex.org/I145847075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063924795"],"corresponding_institution_ids":["https://openalex.org/I145847075"],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47278738,"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":"1175","last_page":"1180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9983000159263611,"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/T12923","display_name":"Digital Image Processing Techniques","score":0.9976999759674072,"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/edit-distance","display_name":"Edit distance","score":0.942127525806427},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8157421946525574},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6868137121200562},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6400268077850342},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6364965438842773},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6339614391326904},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.6296411752700806},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6288199424743652},{"id":"https://openalex.org/keywords/distance-transform","display_name":"Distance transform","score":0.5503119230270386},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.5283792018890381},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47536104917526245},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44980940222740173},{"id":"https://openalex.org/keywords/distance-measures","display_name":"Distance measures","score":0.4187763035297394},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18468263745307922}],"concepts":[{"id":"https://openalex.org/C44359876","wikidata":"https://www.wikidata.org/wiki/Q5338467","display_name":"Edit distance","level":2,"score":0.942127525806427},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8157421946525574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6868137121200562},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6400268077850342},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6364965438842773},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6339614391326904},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.6296411752700806},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6288199424743652},{"id":"https://openalex.org/C73621898","wikidata":"https://www.wikidata.org/wiki/Q2940504","display_name":"Distance transform","level":3,"score":0.5503119230270386},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.5283792018890381},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47536104917526245},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44980940222740173},{"id":"https://openalex.org/C2639959","wikidata":"https://www.wikidata.org/wiki/Q1344778","display_name":"Distance measures","level":2,"score":0.4187763035297394},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18468263745307922},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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":23,"referenced_works":["https://openalex.org/W53843897","https://openalex.org/W1647671624","https://openalex.org/W1770396117","https://openalex.org/W1909740415","https://openalex.org/W1980073965","https://openalex.org/W1981450288","https://openalex.org/W2010541316","https://openalex.org/W2030001217","https://openalex.org/W2065429801","https://openalex.org/W2066792529","https://openalex.org/W2071469153","https://openalex.org/W2116719896","https://openalex.org/W2124351162","https://openalex.org/W2129042353","https://openalex.org/W2144548729","https://openalex.org/W2147141800","https://openalex.org/W2196153064","https://openalex.org/W2341548470","https://openalex.org/W2342204193","https://openalex.org/W3178841571","https://openalex.org/W6636915900","https://openalex.org/W6687348722","https://openalex.org/W7036243320"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2372421320","https://openalex.org/W2057775483","https://openalex.org/W2041871225","https://openalex.org/W2386644571","https://openalex.org/W4205800335","https://openalex.org/W2069148796","https://openalex.org/W2147425361"],"abstract_inverted_index":{"In":[0,22],"this":[1,115],"paper,":[2],"we":[3],"present":[4],"a":[5,18,30,133,136,149],"novel":[6],"distance":[7,28,50,63,68,76],"metric":[8],"called":[9],"Segmentation":[10],"Edit":[11],"Distance":[12],"(SED)":[13],"and":[14,33,125,146,155],"its":[15],"use":[16],"as":[17,59],"segmentation":[19,23,32,38,91,137,169],"evaluation":[20,138,170],"metric.":[21,151],"evaluation,":[24],"the":[25,34,52,60,65,70,80,98,111,122,126,142,167],"difference":[26],"or":[27,64],"of":[29,54,72,82,100,121,144,166],"test":[31],"associated":[35],"ground":[36],"truth":[37],"are":[39,86,117],"measured":[40],"in":[41],"order":[42],"to":[43,69,88,107,114,131,163],"compare":[44],"different":[45],"algorithms.":[46],"Our":[47],"proposed":[48],"edit":[49,56,62,67,83,96,109],"extends":[51],"idea":[53],"other":[55,108,164],"distances":[57],"such":[58],"string":[61],"graph":[66],"domain":[71],"image":[73,127],"segmentations.":[74],"The":[75],"is":[77,104],"based":[78,118],"on":[79,119,153],"cost":[81],"operations":[84],"that":[85],"needed":[87],"transform":[89],"one":[90,95],"into":[92,148],"another.":[93],"Only":[94],"operation,":[97],"deletion":[99],"an":[101],"error":[102,123],"region,":[103],"considered.":[105],"Different":[106],"distances,":[110],"costs":[112],"assigned":[113],"operation":[116],"properties":[120],"regions":[124],"processing":[128],"method":[129],"used":[130],"delete":[132],"region.":[134],"As":[135],"metric,":[139],"it":[140],"combines":[141],"assessment":[143],"accuracy":[145],"efficiency":[147],"single":[150],"Evaluations":[152],"synthetic":[154],"real":[156],"world":[157],"data":[158],"show":[159],"promising":[160],"results":[161],"compared":[162],"state":[165],"art":[168],"metrics.":[171]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}