{"id":"https://openalex.org/W2970372567","doi":"https://doi.org/10.1109/icinfa.2018.8812365","title":"Research on color image segmentation algorithm based on improved Grab-cut","display_name":"Research on color image segmentation algorithm based on improved Grab-cut","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2970372567","doi":"https://doi.org/10.1109/icinfa.2018.8812365","mag":"2970372567"},"language":"en","primary_location":{"id":"doi:10.1109/icinfa.2018.8812365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icinfa.2018.8812365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Information and Automation (ICIA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5082305702","display_name":"Xv Han","orcid":"https://orcid.org/0000-0003-3562-2822"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xv Han","raw_affiliation_strings":["Beijing Institute of Graphic Communication, Daxing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication, Daxing, Beijing, China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101914316","display_name":"Yali Qi","orcid":"https://orcid.org/0000-0001-7779-5356"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yali Qi","raw_affiliation_strings":["Beijing Institute of Graphic Communication, Daxing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication, Daxing, Beijing, China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100547956","display_name":"Huayan Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huayan Sun","raw_affiliation_strings":["Beijing Institute of Graphic Communication, Daxing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication, Daxing, Beijing, China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086323231","display_name":"Yunfei Zi","orcid":"https://orcid.org/0000-0002-4778-7109"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfei Zi","raw_affiliation_strings":["Beijing Institute of Graphic Communication, Daxing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication, Daxing, Beijing, China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100631362","display_name":"Yuanhao Li","orcid":"https://orcid.org/0000-0002-6938-5012"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanhao Li","raw_affiliation_strings":["Beijing Institute of Graphic Communication, Daxing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication, Daxing, Beijing, China","institution_ids":["https://openalex.org/I4210135483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210135483"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.998199999332428,"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":0.998199999332428,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9976000189781189,"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":0.9951000213623047,"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/sobel-operator","display_name":"Sobel operator","score":0.7885698080062866},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6749781966209412},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6365488171577454},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6161527633666992},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.6093817949295044},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.607612669467926},{"id":"https://openalex.org/keywords/deriche-edge-detector","display_name":"Deriche edge detector","score":0.5782148838043213},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5727176070213318},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5656579732894897},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.5521062612533569},{"id":"https://openalex.org/keywords/image-gradient","display_name":"Image gradient","score":0.5095070004463196},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5016744136810303},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.499042272567749},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.44331586360931396},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4379425346851349},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42840689420700073},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.3472091555595398},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28679990768432617},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2219368815422058}],"concepts":[{"id":"https://openalex.org/C30703548","wikidata":"https://www.wikidata.org/wiki/Q1757673","display_name":"Sobel operator","level":5,"score":0.7885698080062866},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6749781966209412},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6365488171577454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6161527633666992},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.6093817949295044},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.607612669467926},{"id":"https://openalex.org/C167074055","wikidata":"https://www.wikidata.org/wiki/Q5262565","display_name":"Deriche edge detector","level":5,"score":0.5782148838043213},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5727176070213318},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5656579732894897},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.5521062612533569},{"id":"https://openalex.org/C182037307","wikidata":"https://www.wikidata.org/wiki/Q17039097","display_name":"Image gradient","level":5,"score":0.5095070004463196},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5016744136810303},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.499042272567749},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.44331586360931396},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4379425346851349},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42840689420700073},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.3472091555595398},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28679990768432617},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2219368815422058},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icinfa.2018.8812365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icinfa.2018.8812365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Information and Automation (ICIA)","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":17,"referenced_works":["https://openalex.org/W79315950","https://openalex.org/W327560106","https://openalex.org/W1586609313","https://openalex.org/W2084684409","https://openalex.org/W2089673850","https://openalex.org/W2112421114","https://openalex.org/W2113137767","https://openalex.org/W2124351162","https://openalex.org/W2143516773","https://openalex.org/W2147188488","https://openalex.org/W2152216760","https://openalex.org/W2169551590","https://openalex.org/W2213946457","https://openalex.org/W2387011497","https://openalex.org/W4244424643","https://openalex.org/W6671856327","https://openalex.org/W6688172910"],"related_works":["https://openalex.org/W4312567938","https://openalex.org/W2028915339","https://openalex.org/W2371586031","https://openalex.org/W2367298911","https://openalex.org/W1974517722","https://openalex.org/W2356242818","https://openalex.org/W2545393398","https://openalex.org/W2379894902","https://openalex.org/W2354100707","https://openalex.org/W3092835544"],"abstract_inverted_index":{"Because":[0],"the":[1,7,12,15,18,21,46,53,57,61,68,75,84,88,92,96,103],"Grab-cut":[2],"algorithm":[3,86],"mainly":[4],"relies":[5],"on":[6],"region":[8],"information":[9,48],"and":[10,27,99],"lacks":[11],"edge":[13,16,47,54,63,97],"information,":[14],"of":[17,45,56,77,91,102],"extraction":[19],"in":[20,39,71],"segmentation":[22,93],"result":[23],"is":[24,37,50,65],"not":[25],"smooth":[26],"accurate":[28],"enough.":[29],"Aiming":[30],"at":[31],"this":[32,40],"disadvantage,":[33],"an":[34],"improved":[35,85],"method":[36],"proposed":[38],"paper.":[41],"A":[42],"certain":[43,101],"proportion":[44],"which":[49],"acquired":[51],"after":[52],"detection":[55],"image":[58],"by":[59],"using":[60],"Sobel":[62],"operator":[64],"added":[66],"to":[67,73],"smoothness":[69,98],"term,":[70],"order":[72],"improve":[74],"reliability":[76],"smoothing":[78],"constraint.":[79],"Experimental":[80],"results":[81],"show":[82],"that":[83],"reduces":[87],"error":[89],"rate":[90],"result,":[94],"improves":[95],"eliminates":[100],"noise":[104],"boundary.":[105]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}