{"id":"https://openalex.org/W2112290246","doi":"https://doi.org/10.1109/iccv.2009.5459195","title":"Graph cuts using a Riemannian metric induced by tensor voting","display_name":"Graph cuts using a Riemannian metric induced by tensor voting","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W2112290246","doi":"https://doi.org/10.1109/iccv.2009.5459195","mag":"2112290246"},"language":"en","primary_location":{"id":"doi:10.1109/iccv.2009.5459195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2009.5459195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision","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/A5041283782","display_name":"Hyung Il Koo","orcid":"https://orcid.org/0000-0002-6955-8083"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyung Il Koo","raw_affiliation_strings":["Department of EECS, INMC, Seoul National University, Seoul, South Korea","Department of EECS, INMC, Seoul National University, 151-742, Korea"],"affiliations":[{"raw_affiliation_string":"Department of EECS, INMC, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Department of EECS, INMC, Seoul National University, 151-742, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055171648","display_name":"Nam Ik Cho","orcid":"https://orcid.org/0000-0001-5297-4649"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nam Ik Cho","raw_affiliation_strings":["Department of EECS, INMC, Seoul National University, Seoul, South Korea","Department of EECS, INMC, Seoul National University, 151-742, Korea"],"affiliations":[{"raw_affiliation_string":"Department of EECS, INMC, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Department of EECS, INMC, Seoul National University, 151-742, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041283782"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":0.6471,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.71852342,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"24","issue":null,"first_page":"514","last_page":"520"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation 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/T10052","display_name":"Medical Image Segmentation 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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/T11605","display_name":"Visual Attention and Saliency Detection","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"}}],"keywords":[{"id":"https://openalex.org/keywords/fundamental-theorem-of-riemannian-geometry","display_name":"Fundamental theorem of Riemannian geometry","score":0.5505794882774353},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5060462355613708},{"id":"https://openalex.org/keywords/cut","display_name":"Cut","score":0.4904036521911621},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4537820816040039},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.4371698796749115},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41281306743621826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3993165194988251},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.33006513118743896},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.31285715103149414},{"id":"https://openalex.org/keywords/ricci-curvature","display_name":"Ricci curvature","score":0.29108908772468567},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.25416842103004456},{"id":"https://openalex.org/keywords/curvature","display_name":"Curvature","score":0.185692697763443},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.10322576761245728}],"concepts":[{"id":"https://openalex.org/C4306028","wikidata":"https://www.wikidata.org/wiki/Q2185349","display_name":"Fundamental theorem of Riemannian geometry","level":4,"score":0.5505794882774353},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5060462355613708},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.4904036521911621},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4537820816040039},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4371698796749115},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41281306743621826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3993165194988251},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33006513118743896},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.31285715103149414},{"id":"https://openalex.org/C12089564","wikidata":"https://www.wikidata.org/wiki/Q1195879","display_name":"Ricci curvature","level":3,"score":0.29108908772468567},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.25416842103004456},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.185692697763443},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.10322576761245728},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccv.2009.5459195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2009.5459195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W84374482","https://openalex.org/W1499695691","https://openalex.org/W1608008825","https://openalex.org/W1967956032","https://openalex.org/W2105365768","https://openalex.org/W2106569125","https://openalex.org/W2111559961","https://openalex.org/W2113137767","https://openalex.org/W2117740473","https://openalex.org/W2118295753","https://openalex.org/W2121743855","https://openalex.org/W2121947440","https://openalex.org/W2125315716","https://openalex.org/W2125637308","https://openalex.org/W2128089874","https://openalex.org/W2131272198","https://openalex.org/W2135705692","https://openalex.org/W2138165282","https://openalex.org/W2140789035","https://openalex.org/W2140865211","https://openalex.org/W2143516773","https://openalex.org/W2145861118","https://openalex.org/W2146228873","https://openalex.org/W2147899407","https://openalex.org/W2155989469","https://openalex.org/W2161758457","https://openalex.org/W2169527406","https://openalex.org/W2169551590","https://openalex.org/W2171187077","https://openalex.org/W6603484826","https://openalex.org/W6679426272","https://openalex.org/W6680547170","https://openalex.org/W6680866351"],"related_works":["https://openalex.org/W2103111323","https://openalex.org/W2735206154","https://openalex.org/W3125543343","https://openalex.org/W4379507457","https://openalex.org/W2109407305","https://openalex.org/W2032319136","https://openalex.org/W1582388844","https://openalex.org/W2088651901","https://openalex.org/W2897997384","https://openalex.org/W1544828638"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,64],"present":[4],"a":[5,22,26,75],"new":[6],"algorithm":[7,110],"that":[8,66],"combines":[9],"the":[10,52,67,82,88,97,102,107,126],"advantages":[11],"of":[12,28,37,54,91,116,128],"tensor":[13,55,68,72,92],"voting":[14,19,56,73],"into":[15,57,101],"graph":[16,46,103,108],"cuts.":[17,47],"Tensor":[18],"has":[20],"been":[21],"popular":[23],"tool":[24],"for":[25],"number":[27],"early":[29],"vision":[30],"problems":[31],"since":[32],"it":[33],"can":[34,111,122],"use":[35],"principles":[36,115],"perceptual":[38,117],"grouping,":[39],"which":[40],"are":[41],"not":[42],"well":[43],"considered":[44],"in":[45,78,125],"We":[48],"attempt":[49],"to":[50,87],"encode":[51],"power":[53],"an":[58],"energy":[59],"minimization":[60],"framework.":[61],"For":[62],"this,":[63],"assume":[65],"map":[69],"obtained":[70],"by":[71,95],"induces":[74],"Riemannian":[76,99],"metric":[77,83,100],"image":[79],"domain,":[80],"and":[81,137],"is":[84],"constructed":[85],"according":[86],"conventional":[89],"ways":[90],"interpretation.":[93],"Finally,":[94],"embedding":[96],"induced":[98],"via":[104],"edge":[105,135],"weights,":[106],"cuts":[109],"have":[112],"priors":[113],"considering":[114],"grouping.":[118],"The":[119],"proposed":[120],"method":[121],"be":[123],"used":[124],"labeling":[127],"occluded":[129],"regions,":[130],"object":[131],"segmentation":[132],"using":[133],"only":[134],"information,":[136],"boundary":[138],"regularization.":[139]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}