{"id":"https://openalex.org/W2507380563","doi":"https://doi.org/10.1109/lsp.2016.2605133","title":"Interactive Stereo Image Segmentation With RGB-D Hybrid Constraints","display_name":"Interactive Stereo Image Segmentation With RGB-D Hybrid Constraints","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2507380563","doi":"https://doi.org/10.1109/lsp.2016.2605133","mag":"2507380563"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2016.2605133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2016.2605133","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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":null,"display_name":"Wei Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Ma","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100786675","display_name":"Yue Qin","orcid":"https://orcid.org/0000-0003-1691-8783"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Qin","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061887584","display_name":"Luwei Yang","orcid":"https://orcid.org/0000-0003-0358-0007"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luwei Yang","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011919230","display_name":"Shibiao Xu","orcid":"https://orcid.org/0000-0003-4037-9900"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shibiao Xu","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100614065","display_name":"Xiaopeng Zhang","orcid":"https://orcid.org/0000-0001-6337-5748"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaopeng Zhang","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8425,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.78495139,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"23","issue":"11","first_page":"1533","last_page":"1537"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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/T10531","display_name":"Advanced Vision and Imaging","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.805209219455719},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7084974646568298},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6646780371665955},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.647253155708313},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6050900816917419},{"id":"https://openalex.org/keywords/cut","display_name":"Cut","score":0.5986151695251465},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5658029913902283},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5255457162857056},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4932233691215515},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4673391282558441},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.46681591868400574},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.421272337436676},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38593119382858276},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3075038194656372},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.07314273715019226}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.805209219455719},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7084974646568298},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6646780371665955},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.647253155708313},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6050900816917419},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.5986151695251465},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5658029913902283},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5255457162857056},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4932233691215515},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4673391282558441},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.46681591868400574},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.421272337436676},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38593119382858276},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3075038194656372},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.07314273715019226},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2016.2605133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2016.2605133","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1865144911","display_name":"\u57fa\u4e8e\u4f53\u611f\u7684\u65b0\u578b\u4e92\u52a8\u8ba1\u7b97\u7406\u8bba\u3001\u65b9\u6cd5\u4e0e\u5173\u952e\u6280\u672f\u7684\u7814\u7a76\u4e0e\u5e94\u7528","funder_award_id":"61332017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6398121296","display_name":"\u9ad8\u65f6\u7a7a\u5206\u8fa8\u7387\u7684\u52a8\u6001\u5bf9\u8c61\u51e0\u4f55\u4e0e\u8fd0\u52a8\u4fe1\u606f\u83b7\u53d6\u65b9\u6cd5","funder_award_id":"61271430","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6961195082","display_name":null,"funder_award_id":"4152006","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W23666710","https://openalex.org/W1092731009","https://openalex.org/W1559395077","https://openalex.org/W1781337833","https://openalex.org/W1964884769","https://openalex.org/W1970835671","https://openalex.org/W1982025739","https://openalex.org/W1999478155","https://openalex.org/W2027173352","https://openalex.org/W2097350296","https://openalex.org/W2113201962","https://openalex.org/W2114542651","https://openalex.org/W2124351162","https://openalex.org/W2137251023","https://openalex.org/W2138454898","https://openalex.org/W2151103935","https://openalex.org/W2162509854","https://openalex.org/W2169551590","https://openalex.org/W2213003429","https://openalex.org/W2289053825","https://openalex.org/W2294819727","https://openalex.org/W3136174539","https://openalex.org/W6695949810"],"related_works":["https://openalex.org/W2375137989","https://openalex.org/W2010171670","https://openalex.org/W2109407305","https://openalex.org/W2032319136","https://openalex.org/W2088651901","https://openalex.org/W2897997384","https://openalex.org/W1544828638","https://openalex.org/W2086619084","https://openalex.org/W2000036183","https://openalex.org/W2049858394"],"abstract_inverted_index":{"This":[0],"letter":[1],"presents":[2],"an":[3],"approach":[4],"to":[5,43,117],"extracting":[6],"a":[7,12,19,22,54],"target":[8],"object":[9,27],"interactively":[10],"from":[11,84],"given":[13],"pair":[14],"of":[15,25,32,48,64,79,90,115],"stereo":[16,141],"images.":[17],"First,":[18],"user":[20],"marks":[21],"few":[23],"parts":[24],"the":[26,33,45,49,62,80,85,91,100,109,119,123],"and":[28,51,71,102,121],"background":[29],"in":[30,105,129],"either":[31],"two":[34,77,86],"views":[35,87],"with":[36,58,143],"strokes.":[37],"The":[38,132],"marked":[39],"pixels":[40,70,128],"are":[41,82],"used":[42],"generate":[44],"prior":[46],"models":[47],"foreground":[50,81],"background.":[52],"Second,":[53],"graph":[55,92,94],"is":[56],"constructed":[57],"constraints":[59,139],"formulated":[60],"by":[61,88,149],"priors":[63,101,120],"foreground/background,":[65],"similarities":[66,104],"between":[67,73,126],"intraview":[68],"neighbor":[69,103,127],"correspondences":[72],"interview":[74],"pixels.":[75],"Third,":[76],"segments":[78,142],"extracted":[83],"optimization":[89],"via":[93],"cut.":[95],"Traditional":[96],"methods":[97],"generally":[98],"define":[99],"RGB":[106],"space.":[107,131],"Differently,":[108],"proposed":[110,133],"method":[111,134],"integrates":[112],"disparity":[113],"distributions":[114],"foreground/background":[116],"enrich":[118],"defines":[122],"similarity":[124],"metric":[125],"RGB-D":[130,137],"that":[135],"utilizes":[136],"hybrid":[138],"generates":[140],"accuracies":[144],"higher":[145],"than":[146],"those":[147],"obtained":[148],"state-of-the-art":[150],"methods.":[151]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
