{"id":"https://openalex.org/W2043293910","doi":"https://doi.org/10.1109/icip.2014.7025123","title":"Getting a morphological tree of shapes for multivariate images: Paths, traps, and pitfalls","display_name":"Getting a morphological tree of shapes for multivariate images: Paths, traps, and pitfalls","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2043293910","doi":"https://doi.org/10.1109/icip.2014.7025123","mag":"2043293910"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2014.7025123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2014.7025123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"preprint","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/A5059793245","display_name":"Edwin Carlinet","orcid":"https://orcid.org/0000-0001-5737-5266"},"institutions":[{"id":"https://openalex.org/I4210138808","display_name":"\u00c9cole Pour l'Informatique et les Techniques Avanc\u00e9es","ror":"https://ror.org/04r5ddq29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210138808"]},{"id":"https://openalex.org/I4210152518","display_name":"Laboratoire d'Informatique Gaspard-Monge","ror":"https://ror.org/04t50yk91","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I142631665","https://openalex.org/I4210145102","https://openalex.org/I4210152518","https://openalex.org/I4210154111","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Edwin Carlinet","raw_affiliation_strings":["EPITA Research and Development Lab. (LRDE), Le Kremlin-Bic\u00eatre, France","Universit\u00e9 Paris-Est, Laboratoire d'Informatique Gaspard-Monge (LIGM), Noisy-le-Grand, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPITA Research and Development Lab. (LRDE), Le Kremlin-Bic\u00eatre, France","institution_ids":["https://openalex.org/I4210138808"]},{"raw_affiliation_string":"Universit\u00e9 Paris-Est, Laboratoire d'Informatique Gaspard-Monge (LIGM), Noisy-le-Grand, France","institution_ids":["https://openalex.org/I4210152518"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068813606","display_name":"Thierry G\u00e9raud","orcid":"https://orcid.org/0000-0002-0380-7948"},"institutions":[{"id":"https://openalex.org/I4210138808","display_name":"\u00c9cole Pour l'Informatique et les Techniques Avanc\u00e9es","ror":"https://ror.org/04r5ddq29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210138808"]},{"id":"https://openalex.org/I4210152518","display_name":"Laboratoire d'Informatique Gaspard-Monge","ror":"https://ror.org/04t50yk91","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I142631665","https://openalex.org/I4210145102","https://openalex.org/I4210152518","https://openalex.org/I4210154111","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Thierry Geraud","raw_affiliation_strings":["EPITA Research and Development Lab. (LRDE), Le Kremlin-Bic\u00eatre, France","Universit\u00e9 Paris-Est, Laboratoire d'Informatique Gaspard-Monge (LIGM), Noisy-le-Grand, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPITA Research and Development Lab. (LRDE), Le Kremlin-Bic\u00eatre, France","institution_ids":["https://openalex.org/I4210138808"]},{"raw_affiliation_string":"Universit\u00e9 Paris-Est, Laboratoire d'Informatique Gaspard-Monge (LIGM), Noisy-le-Grand, France","institution_ids":["https://openalex.org/I4210152518"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4556,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.85461248,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"615","last_page":"619"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.996999979019165,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.996999979019165,"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/T11666","display_name":"Color Science and Applications","score":0.9822999835014343,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9786999821662903,"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/computer-science","display_name":"Computer science","score":0.7048928737640381},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.658953070640564},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.5995185375213623},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5920699238777161},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5880258083343506},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.564063549041748},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5515109896659851},{"id":"https://openalex.org/keywords/tree-structure","display_name":"Tree structure","score":0.489776611328125},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.44975346326828003},{"id":"https://openalex.org/keywords/mathematical-morphology","display_name":"Mathematical morphology","score":0.43137669563293457},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4292139410972595},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38066768646240234},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32303300499916077},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26652058959007263},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.21559369564056396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17002525925636292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7048928737640381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.658953070640564},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.5995185375213623},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5920699238777161},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5880258083343506},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.564063549041748},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5515109896659851},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.489776611328125},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.44975346326828003},{"id":"https://openalex.org/C185568154","wikidata":"https://www.wikidata.org/wiki/Q530242","display_name":"Mathematical morphology","level":4,"score":0.43137669563293457},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4292139410972595},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38066768646240234},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32303300499916077},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26652058959007263},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.21559369564056396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17002525925636292},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icip.2014.7025123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2014.7025123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.711.7134","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.711.7134","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://www.lrde.epita.fr/%7Etheo/papers/geraud.2014.icip.carlinet.pdf","raw_type":"text"},{"id":"pmh:oai:HAL:hal-01476227v1","is_oa":false,"landing_page_url":"https://inria.hal.science/hal-01476227","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"21st IEEE International Conference on Image Processing (ICIP), Oct 2017, Paris, France. pp.615 - 619, ⟨10.1109/ICIP.2014.7025123⟩","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1280534","https://openalex.org/W53267500","https://openalex.org/W138525918","https://openalex.org/W151607323","https://openalex.org/W163640015","https://openalex.org/W200963588","https://openalex.org/W642169424","https://openalex.org/W1489042383","https://openalex.org/W1520230319","https://openalex.org/W1539437305","https://openalex.org/W1570477388","https://openalex.org/W1840243295","https://openalex.org/W1975610128","https://openalex.org/W2003290691","https://openalex.org/W2012050936","https://openalex.org/W2024422416","https://openalex.org/W2047076586","https://openalex.org/W2055535946","https://openalex.org/W2062486658","https://openalex.org/W2074585180","https://openalex.org/W2075283854","https://openalex.org/W2109853861","https://openalex.org/W2111311839","https://openalex.org/W2146842130","https://openalex.org/W2157691228","https://openalex.org/W2169157404","https://openalex.org/W2184563774","https://openalex.org/W2915008565","https://openalex.org/W6602155143","https://openalex.org/W6631171081","https://openalex.org/W6634234112","https://openalex.org/W6638757475","https://openalex.org/W6653349598","https://openalex.org/W6684794552"],"related_works":["https://openalex.org/W4318664220","https://openalex.org/W2771047279","https://openalex.org/W4388409104","https://openalex.org/W2124951708","https://openalex.org/W2058740282","https://openalex.org/W2375111338","https://openalex.org/W2220864757","https://openalex.org/W2352304637","https://openalex.org/W4282813639","https://openalex.org/W4309911935"],"abstract_inverted_index":{"The":[0],"tree":[1,7,77],"of":[2,14,38,78,119],"shapes":[3,79],"is":[4,47],"a":[5,97],"morphological":[6],"that":[8],"provides":[9],"an":[10],"high-level":[11],"hierarchical":[12],"representation":[13],"the":[15,26,36,39,76,116,120],"image":[16,20],"suitable":[17],"for":[18],"many":[19,52],"processing":[21],"tasks.":[22],"This":[23],"structure":[24],"has":[25],"desirable":[27],"properties":[28],"to":[29,74,108,124],"be":[30],"self-dual":[31],"and":[32,34,95,112,114],"contrast-invariant":[33],"describes":[35],"organization":[37],"objects":[40],"through":[41,106],"level":[42],"lines":[43],"inclusion.":[44],"Yet":[45],"it":[46],"defined":[48],"on":[49,80,86,93],"gray-level":[50],"while":[51],"images":[53],"have":[54],"multivariate":[55],"data":[56],"(color":[57],"images,":[58],"multispectral":[59],"images.)":[60],"where":[61],"information":[62],"are":[63],"split":[64],"across":[65],"channels.":[66],"In":[67],"this":[68],"paper,":[69],"we":[70,102],"propose":[71],"some":[72],"leads":[73],"extend":[75],"colors":[81],"with":[82],"classical":[83,125],"approaches":[84,91,105],"based":[85,92],"total":[87],"orders,":[88],"more":[89],"recent":[90],"graphs":[94],"also":[96],"new":[98,121],"distance-based":[99],"method.":[100],"Eventually,":[101],"compare":[103],"these":[104],"denoising":[107],"highlight":[109],"their":[110],"strengths":[111],"weaknesses":[113],"show":[115],"strong":[117],"potential":[118],"methods":[122],"compared":[123],"ones.":[126]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}