{"id":"https://openalex.org/W1919033285","doi":"https://doi.org/10.1109/cvpr.2015.7298974","title":"Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images","display_name":"Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1919033285","doi":"https://doi.org/10.1109/cvpr.2015.7298974","mag":"1919033285"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298974","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298974","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5068872648","display_name":"Dan Banica","orcid":null},"institutions":[{"id":"https://openalex.org/I4210120266","display_name":"Czech Academy of Sciences, Institute of Mathematics","ror":"https://ror.org/02tv1yf50","country_code":"CZ","type":"facility","lineage":["https://openalex.org/I202391551","https://openalex.org/I4210120266"]},{"id":"https://openalex.org/I58077936","display_name":"Romanian Academy","ror":"https://ror.org/0561n6946","country_code":"RO","type":"archive","lineage":["https://openalex.org/I58077936"]}],"countries":["CZ","RO"],"is_corresponding":true,"raw_author_name":"Dan Banica","raw_affiliation_strings":["Institute of Mathematics of the Romanian Academy"],"affiliations":[{"raw_affiliation_string":"Institute of Mathematics of the Romanian Academy","institution_ids":["https://openalex.org/I58077936","https://openalex.org/I4210120266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007658897","display_name":"Cristian Sminchisescu","orcid":"https://orcid.org/0000-0001-5256-886X"},"institutions":[{"id":"https://openalex.org/I4210120266","display_name":"Czech Academy of Sciences, Institute of Mathematics","ror":"https://ror.org/02tv1yf50","country_code":"CZ","type":"facility","lineage":["https://openalex.org/I202391551","https://openalex.org/I4210120266"]},{"id":"https://openalex.org/I187531555","display_name":"Lund University","ror":"https://ror.org/012a77v79","country_code":"SE","type":"education","lineage":["https://openalex.org/I187531555"]},{"id":"https://openalex.org/I58077936","display_name":"Romanian Academy","ror":"https://ror.org/0561n6946","country_code":"RO","type":"archive","lineage":["https://openalex.org/I58077936"]}],"countries":["CZ","RO","SE"],"is_corresponding":false,"raw_author_name":"Cristian Sminchisescu","raw_affiliation_strings":["Institute of Mathematics of the Romanian Academy","Lund University"],"affiliations":[{"raw_affiliation_string":"Institute of Mathematics of the Romanian Academy","institution_ids":["https://openalex.org/I58077936","https://openalex.org/I4210120266"]},{"raw_affiliation_string":"Lund University","institution_ids":["https://openalex.org/I187531555"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068872648"],"corresponding_institution_ids":["https://openalex.org/I4210120266","https://openalex.org/I58077936"],"apc_list":null,"apc_paid":null,"fwci":5.9854,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.97452951,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3517","last_page":"3526"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991999864578247,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991999864578247,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7295095920562744},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6924211978912354},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6593155860900879},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5883859395980835},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5661441087722778},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5618457198143005},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49881768226623535},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4690445065498352},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44681882858276367},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44585609436035156},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4382782280445099},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36438342928886414},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14781281352043152}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7295095920562744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6924211978912354},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6593155860900879},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5883859395980835},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5661441087722778},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5618457198143005},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49881768226623535},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4690445065498352},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44681882858276367},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44585609436035156},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4382782280445099},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36438342928886414},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14781281352043152},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2015.7298974","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298974","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.718.2687","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.718.2687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.maths.lth.se/matematiklth/personal/sminchis/papers/dbcs-rgbd15.pdf","raw_type":"text"},{"id":"pmh:oai:lup.lub.lu.se:14ee5cbd-f538-4609-8cce-4491f15ef696","is_oa":false,"landing_page_url":"https://lup.lub.lu.se/record/14ee5cbd-f538-4609-8cce-4491f15ef696","pdf_url":null,"source":{"id":"https://openalex.org/S4306400536","display_name":"Lund University Publications (Lund University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I187531555","host_organization_name":"Lund University","host_organization_lineage":["https://openalex.org/I187531555"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W44865868","https://openalex.org/W51625820","https://openalex.org/W78159342","https://openalex.org/W125693051","https://openalex.org/W1483240902","https://openalex.org/W1486646730","https://openalex.org/W1555385401","https://openalex.org/W1564871316","https://openalex.org/W1565402342","https://openalex.org/W1606858007","https://openalex.org/W1764552418","https://openalex.org/W1980586251","https://openalex.org/W1989784484","https://openalex.org/W1994259434","https://openalex.org/W2018299767","https://openalex.org/W2020733616","https://openalex.org/W2032293070","https://openalex.org/W2037227137","https://openalex.org/W2046382188","https://openalex.org/W2053973158","https://openalex.org/W2060280062","https://openalex.org/W2065877784","https://openalex.org/W2066813062","https://openalex.org/W2067912884","https://openalex.org/W2084635560","https://openalex.org/W2085411191","https://openalex.org/W2086374833","https://openalex.org/W2087570216","https://openalex.org/W2099606917","https://openalex.org/W2102605133","https://openalex.org/W2103380420","https://openalex.org/W2107023258","https://openalex.org/W2108889042","https://openalex.org/W2110158442","https://openalex.org/W2110306576","https://openalex.org/W2116877738","https://openalex.org/W2125310925","https://openalex.org/W2129587342","https://openalex.org/W2130350952","https://openalex.org/W2139905387","https://openalex.org/W2145567954","https://openalex.org/W2151103935","https://openalex.org/W2152571752","https://openalex.org/W2155494658","https://openalex.org/W2156222070","https://openalex.org/W2158349948","https://openalex.org/W2163605009","https://openalex.org/W2186094539","https://openalex.org/W2402140145","https://openalex.org/W4252621450","https://openalex.org/W6601819125","https://openalex.org/W6602093332","https://openalex.org/W6603109438","https://openalex.org/W6605121731","https://openalex.org/W6628879895","https://openalex.org/W6629070694","https://openalex.org/W6633201533","https://openalex.org/W6633727509","https://openalex.org/W6634014874","https://openalex.org/W6636412649","https://openalex.org/W6666899075","https://openalex.org/W6671567704","https://openalex.org/W6684191040","https://openalex.org/W6686583229","https://openalex.org/W6713014844","https://openalex.org/W6940791371"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W2087343574","https://openalex.org/W2121910908"],"abstract_inverted_index":{"We":[0,187],"focus":[1],"on":[2,9,14,29,111],"the":[3,49,95,154,161,191,195,204,215,249,252],"problem":[4,183],"of":[5,73,83,97,153,156,163,167,190,251],"semantic":[6,181],"segmentation":[7,182],"based":[8,28,110],"RGB-D":[10,180],"data,":[11],"with":[12,184],"emphasis":[13],"analyzing":[15],"cluttered":[16],"indoor":[17],"scenes":[18],"containing":[19],"many":[20],"visual":[21],"categories":[22],"and":[23,34,46,85,92,121,131,141,149,171,207,227],"instances.":[24],"Our":[25,63],"approach":[26],"is":[27],"a":[30,53,59,71,125,164,178,241],"parametric":[31,74],"figureground":[32],"intensity":[33,84,120],"depth-constrained":[35],"proposal":[36,78],"process":[37,133],"that":[38,57,134,223],"generates":[39],"spatial":[40,100,139],"layout":[41],"hypotheses":[42],"at":[43],"multiple":[44,115],"locations":[45],"scales":[47],"in":[48,88,102,137,177,194],"image":[50],"followed":[51],"by":[52,224,254],"sequential":[54],"inference":[55,130],"algorithm":[56],"produces":[58],"complete":[60,146],"scene":[61,147,242],"estimate.":[62],"contributions":[64],"can":[65,236],"be":[66,237],"summarized":[67],"as":[68,158,160],"follows:":[69],"(1)":[70],"generalization":[72],"max":[75],"flow":[76],"figure-ground":[77],"methodology":[79,246],"to":[80,90],"take":[81],"advantage":[82],"depth":[86,122],"information,":[87],"order":[89],"systematically":[91],"efficiently":[93],"generate":[94],"breakpoints":[96],"an":[98],"underlying":[99],"model":[101,217],"polynomial":[103],"time,":[104],"(2)":[105],"new":[106],"region":[107],"description":[108],"methods":[109],"second-order":[112,226],"pooling":[113],"over":[114,231],"features":[116],"constructed":[117],"using":[118,174],"both":[119,169],"channels,":[123],"(3)":[124],"principled":[126],"search-based":[127],"structured":[128],"prediction":[129],"learning":[132,229],"resolves":[135],"conflicts":[136],"overlapping":[138],"partitions":[140],"selects":[142],"regions":[143],"sequentially":[144],"towards":[145],"estimates,":[148],"(4)":[150],"extensive":[151],"evaluation":[152],"impact":[155],"depth,":[157],"well":[159],"effectiveness":[162],"large":[165],"number":[166],"descriptors,":[168],"pre-designed":[170],"automatically":[172],"obtained":[173],"deep":[175,228],"learning,":[176],"difficult":[179],"92":[185],"classes.":[186],"report":[188],"state":[189,250],"art":[192,253],"results":[193],"challenging":[196],"NYU":[197],"Depth":[198],"Dataset":[199],"V2":[200],"[44],":[201],"extended":[202],"for":[203],"RMRC":[205,208],"2013":[206],"2014":[209],"Indoor":[210],"Segmentation":[211],"Challenges,":[212],"where":[213],"currently":[214],"proposed":[216],"ranks":[218],"first.":[219],"Moreover,":[220],"we":[221],"show":[222],"combining":[225],"features,":[230],"15%":[232],"relative":[233],"accuracy":[234],"improvements":[235],"additionally":[238],"achieved.":[239],"In":[240],"classification":[243],"benchmark,":[244],"our":[245],"further":[247],"improves":[248],"24%.":[255]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":14},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}