{"id":"https://openalex.org/W2028360707","doi":"https://doi.org/10.1109/icip.2010.5653139","title":"Separation of overlapped color planes for document images","display_name":"Separation of overlapped color planes for document images","publication_year":2010,"publication_date":"2010-09-01","ids":{"openalex":"https://openalex.org/W2028360707","doi":"https://doi.org/10.1109/icip.2010.5653139","mag":"2028360707"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2010.5653139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2010.5653139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Image Processing","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/A5111710059","display_name":"Danian Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Danian Zheng","raw_affiliation_strings":["Fujitsu Research and Development Company Limited, Beijing, China","Fujitsu R&D Co. Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Fujitsu Research and Development Company Limited, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]},{"raw_affiliation_string":"Fujitsu R&D Co. Ltd, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327195","display_name":"Jun Sun","orcid":"https://orcid.org/0000-0002-0967-4859"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Sun","raw_affiliation_strings":["Fujitsu Research and Development Company Limited, Beijing, China","Fujitsu R&D Co. Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Fujitsu Research and Development Company Limited, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]},{"raw_affiliation_string":"Fujitsu R&D Co. Ltd, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031845777","display_name":"Satoshi Naoi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Satoshi Naoi","raw_affiliation_strings":["Fujitsu Research and Development Company Limited, Beijing, China","Fujitsu R&D Co. Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Fujitsu Research and Development Company Limited, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]},{"raw_affiliation_string":"Fujitsu R&D Co. Ltd, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110596195","display_name":"Misako Suwa","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Misako Suwa","raw_affiliation_strings":["Fujitsu Laboratories Limited, Kawasaki, Japan","FUJITSU LABORATORIES LTD. Kawasaki, Japan"],"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Limited, Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]},{"raw_affiliation_string":"FUJITSU LABORATORIES LTD. Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040886013","display_name":"Hiroaki Takebe","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroaki Takebe","raw_affiliation_strings":["Fujitsu Laboratories Limited, Kawasaki, Japan","FUJITSU LABORATORIES LTD. Kawasaki, Japan"],"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Limited, Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]},{"raw_affiliation_string":"FUJITSU LABORATORIES LTD. Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102252867","display_name":"Yoshinobu Hotta","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshinobu Hotta","raw_affiliation_strings":["Fujitsu Laboratories Limited, Kawasaki, Japan","FUJITSU LABORATORIES LTD. Kawasaki, Japan"],"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Limited, Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]},{"raw_affiliation_string":"FUJITSU LABORATORIES LTD. Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5111710059"],"corresponding_institution_ids":["https://openalex.org/I4210159607"],"apc_list":null,"apc_paid":null,"fwci":0.3187,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58455154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1949","last_page":"1952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9980000257492065,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9980000257492065,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9972000122070312,"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":0.9972000122070312,"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":0.7197859883308411},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6276187300682068},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5752128958702087},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5163118243217468},{"id":"https://openalex.org/keywords/plane","display_name":"Plane (geometry)","score":0.5074388384819031},{"id":"https://openalex.org/keywords/colored","display_name":"Colored","score":0.48084792494773865},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4661632180213928},{"id":"https://openalex.org/keywords/color-balance","display_name":"Color balance","score":0.4337928891181946},{"id":"https://openalex.org/keywords/color-image","display_name":"Color image","score":0.30475813150405884},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26372769474983215},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.22057238221168518},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18798455595970154},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17724984884262085}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7197859883308411},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6276187300682068},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5752128958702087},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5163118243217468},{"id":"https://openalex.org/C17825722","wikidata":"https://www.wikidata.org/wiki/Q17285","display_name":"Plane (geometry)","level":2,"score":0.5074388384819031},{"id":"https://openalex.org/C2778307483","wikidata":"https://www.wikidata.org/wiki/Q5149038","display_name":"Colored","level":2,"score":0.48084792494773865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4661632180213928},{"id":"https://openalex.org/C159784718","wikidata":"https://www.wikidata.org/wiki/Q182571","display_name":"Color balance","level":5,"score":0.4337928891181946},{"id":"https://openalex.org/C142616399","wikidata":"https://www.wikidata.org/wiki/Q5148604","display_name":"Color image","level":4,"score":0.30475813150405884},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26372769474983215},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.22057238221168518},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18798455595970154},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17724984884262085},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2010.5653139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2010.5653139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1980126476","https://openalex.org/W2067191022","https://openalex.org/W2135968459","https://openalex.org/W2136988768","https://openalex.org/W2146402989","https://openalex.org/W2157953942","https://openalex.org/W2158694875","https://openalex.org/W2162080740","https://openalex.org/W2851076652","https://openalex.org/W6999292452","https://openalex.org/W7071283104"],"related_works":["https://openalex.org/W2375495561","https://openalex.org/W3094461234","https://openalex.org/W2349446338","https://openalex.org/W1995359456","https://openalex.org/W1806074025","https://openalex.org/W2383528035","https://openalex.org/W2349299344","https://openalex.org/W2354625492","https://openalex.org/W1987563425","https://openalex.org/W3186568567"],"abstract_inverted_index":{"Color":[0],"plane":[1],"separation":[2,46],"is":[3,48,85],"very":[4],"useful":[5],"in":[6,25,55],"processing":[7],"color":[8,27,43,60],"document":[9],"images.":[10],"Many":[11],"reported":[12],"methods":[13],"take":[14],"it":[15,77],"as":[16,50],"a":[17,32,51],"multi-class":[18],"classification":[19],"problem":[20],"and":[21,65,91,106],"work":[22],"not":[23],"well":[24],"overlapped":[26,42,92],"regions.":[28],"This":[29],"paper":[30],"proposed":[31],"simple":[33],"but":[34],"effective":[35],"linear":[36],"projection":[37],"based":[38],"method":[39],"for":[40],"separating":[41],"planes.":[44],"The":[45],"task":[47],"taken":[49],"probability":[52],"problem,":[53],"i.e.,":[54],"the":[56,66,80,104],"output":[57],"plane,":[58],"target":[59],"should":[61,69],"have":[62,70],"high":[63],"response":[64],"other":[67],"colors":[68,84,96],"low":[71],"response,":[72],"or":[73],"vice":[74],"versa.":[75],"Furthermore,":[76],"assumes":[78],"that":[79],"number":[81],"of":[82,98,108],"foreground":[83],"low,":[86],"typically":[87],"one":[88],"to":[89],"four,":[90],"areas":[93],"contain":[94],"mixed":[95],"instead":[97],"opaque":[99],"covering.":[100],"Experimental":[101],"results":[102],"demonstrate":[103],"effectiveness":[105],"flexibility":[107],"our":[109],"method.":[110]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
