{"id":"https://openalex.org/W2971037559","doi":"https://doi.org/10.1109/icip.2019.8803669","title":"Learning to Plan Semantic Free-Space Boundary","display_name":"Learning to Plan Semantic Free-Space Boundary","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2971037559","doi":"https://doi.org/10.1109/icip.2019.8803669","mag":"2971037559"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803669","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","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/A5069674556","display_name":"Ziyi Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziyi Yin","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076263241","display_name":"Ziyang Song","orcid":"https://orcid.org/0009-0009-6348-8713"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyang Song","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005376529","display_name":"Zejian Yuan","orcid":"https://orcid.org/0000-0001-5548-3634"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zejian Yuan","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069674556"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10805548,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":null,"first_page":"4504","last_page":"4508"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.994700014591217,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7494333386421204},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.7155088186264038},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5525913238525391},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.5183514356613159},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5006299018859863},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.4926062226295471},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47300034761428833},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.46089309453964233},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1648692488670349},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07951140403747559}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7494333386421204},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.7155088186264038},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5525913238525391},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.5183514356613159},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5006299018859863},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.4926062226295471},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47300034761428833},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.46089309453964233},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1648692488670349},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07951140403747559},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"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/icip.2019.8803669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803669","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W33116912","https://openalex.org/W161578567","https://openalex.org/W1518641734","https://openalex.org/W1522301498","https://openalex.org/W1903029394","https://openalex.org/W1966931935","https://openalex.org/W2028606404","https://openalex.org/W2034430035","https://openalex.org/W2040806316","https://openalex.org/W2138361487","https://openalex.org/W2146939114","https://openalex.org/W2166518725","https://openalex.org/W2168311572","https://openalex.org/W2221953250","https://openalex.org/W2332618872","https://openalex.org/W2562137921","https://openalex.org/W2767941964","https://openalex.org/W2902338282","https://openalex.org/W2963505902","https://openalex.org/W2964121744","https://openalex.org/W2964304707","https://openalex.org/W3100019905","https://openalex.org/W6631190155","https://openalex.org/W6703425876"],"related_works":["https://openalex.org/W2393022482","https://openalex.org/W2377346130","https://openalex.org/W2361092061","https://openalex.org/W2319775965","https://openalex.org/W2357314690","https://openalex.org/W2191886813","https://openalex.org/W1986317414","https://openalex.org/W4302420697","https://openalex.org/W2376821690","https://openalex.org/W2789181986"],"abstract_inverted_index":{"Recently,":[0],"free-space":[1,11,34,95],"detection":[2,12,96],"has":[3,121],"attracted":[4],"widespread":[5],"attention.":[6],"Most":[7],"existing":[8],"methods":[9],"treat":[10],"as":[13,61,97],"a":[14,23,36,42,98,122],"semantic":[15,33,58,94],"segmentation":[16],"task.":[17],"In":[18],"this":[19],"paper,":[20],"we":[21,40,91],"propose":[22],"novel":[24],"approach":[25],"to":[26,45,102],"directly":[27],"infer":[28],"the":[29,32,87,93,104,108],"boundary":[30,54,78],"of":[31,56,107],"from":[35],"single":[37],"image.":[38],"Firstly,":[39],"design":[41],"multistage":[43],"CNN":[44,73],"produce":[46],"2D":[47,88],"belief":[48,89],"maps":[49,90],"with":[50],"high":[51],"resolution":[52],"for":[53],"segments":[55],"different":[57],"classes,":[59],"such":[60],"road":[62,67],"boundary,":[63],"vertical":[64],"obstacles":[65],"on":[66,86,114,125],"and":[68,80],"so":[69],"on.":[70],"The":[71,111],"proposed":[72],"architecture":[74],"can":[75],"implicitly":[76],"learn":[77],"structure":[79],"long-range":[81],"spatial":[82,105],"context.":[83],"Then,":[84],"based":[85],"address":[92],"dynamic":[99],"programming":[100],"problem":[101],"ensure":[103],"smoothness":[106],"predicted":[109],"boundary.":[110],"experimental":[112],"results":[113],"our":[115,119],"dataset":[116],"show":[117],"that":[118],"method":[120],"convincing":[123],"performance":[124],"various":[126],"quantitative":[127],"metrics.":[128]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
