{"id":"https://openalex.org/W6945244905","doi":"https://doi.org/10.2312/vmv.20231235","title":"Semantic Image Abstraction using Panoptic Segmentation for Robotic Painting","display_name":"Semantic Image Abstraction using Panoptic Segmentation for Robotic Painting","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W6945244905","doi":"https://doi.org/10.2312/vmv.20231235"},"language":"en","primary_location":{"id":"doi:10.2312/vmv.20231235","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vmv.20231235","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":""},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/vmv.20231235","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Stroh, Michael","orcid":"https://orcid.org/0009-0001-5156-4549"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Stroh, Michael","raw_affiliation_strings":["University of Konstanz, Germany"],"affiliations":[{"raw_affiliation_string":"University of Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"middle","author":{"id":null,"display_name":"G\u00fclzow, Marvin","orcid":"https://orcid.org/0000-0003-0284-762X"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"G\u00fclzow, Marvin","raw_affiliation_strings":["University of Konstanz, Germany"],"affiliations":[{"raw_affiliation_string":"University of Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"last","author":{"id":null,"display_name":"Deussen, Oliver","orcid":"https://orcid.org/0000-0001-5803-2185"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Deussen, Oliver","raw_affiliation_strings":["University of Konstanz, Germany"],"affiliations":[{"raw_affiliation_string":"University of Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I189712700"],"apc_list":null,"apc_paid":null,"fwci":1.0108,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.86284289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.4016000032424927,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.4016000032424927,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.3578999936580658,"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/T12650","display_name":"Aesthetic Perception and Analysis","score":0.05730000138282776,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6301000118255615},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5235000252723694},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.47189998626708984},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4090999960899353},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.38100001215934753},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.35370001196861267},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3449000120162964},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3398999869823456}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7455000281333923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7070000171661377},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6301000118255615},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5702000260353088},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5235000252723694},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4090999960899353},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2973000109195709},{"id":"https://openalex.org/C2781289151","wikidata":"https://www.wikidata.org/wiki/Q2903989","display_name":"Class hierarchy","level":3,"score":0.28999999165534973},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2312/vmv.20231235","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vmv.20231235","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":""}],"best_oa_location":{"id":"doi:10.2312/vmv.20231235","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vmv.20231235","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":""},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,138,154],"propose":[1],"a":[2,50,71,92,108,115,167],"comprehensive":[3],"pipeline":[4,20],"for":[5,15,60,234],"generating":[6],"adaptable":[7],"image":[8,94,101,176,231],"abstractions":[9,232],"from":[10,30],"input":[11],"pictures,":[12],"tailored":[13],"explicitly":[14],"robotic":[16,235],"painting":[17,236],"tasks.":[18],"Our":[19],"addresses":[21],"several":[22],"key":[23],"objectives,":[24],"including":[25],"the":[26,56,64,75,80,84,100,120,174,180,194,197],"ability":[27],"to":[28,32,54,107,126,141,146,204],"paint":[29],"background":[31],"foreground,":[33],"maintain":[34],"fine":[35,215],"details,":[36,216],"capture":[37],"structured":[38,226],"regions":[39,129,224],"accurately,":[40],"and":[41,135,193,221,238],"highlight":[42],"important":[43],"objects.":[44],"To":[45,172],"achieve":[46],"this,":[47],"we":[48,88,113,178,212],"employ":[49],"panoptic":[51],"segmentation":[52,86,121],"network":[53],"predict":[55],"semantic":[57,85,110,136,148],"class":[58],"membership":[59],"each":[61,105,200],"pixel":[62],"in":[63,79,166,202,230],"image.":[65],"This":[66,97,228],"step":[67],"provides":[68],"us":[69,125],"with":[70,91,225],"detailed":[72],"understanding":[73],"of":[74,199],"object":[76],"categories":[77],"present":[78],"scene.":[81],"Building":[82],"upon":[83],"results,":[87,122],"combine":[89,223],"them":[90],"color-based":[93],"over-segmentation":[95],"technique.":[96],"process":[98],"partitions":[99],"into":[102],"monochromatic":[103],"regions,":[104],"corresponding":[106],"specific":[109],"object.":[111],"Next,":[112],"construct":[114],"hierarchical":[116,169,181],"tree":[117,183],"based":[118,130],"on":[119,131],"which":[123],"allows":[124],"merge":[127],"adjacent":[128],"their":[132,205],"color":[133,189],"difference":[134],"class.":[137],"take":[139],"care":[140],"ensure":[142],"that":[143],"shapes":[144],"belonging":[145],"different":[147],"objects":[149],"are":[150,163],"not":[151],"merged":[152],"together.":[153],"iteratively":[155],"perform":[156],"adjacency":[157],"merging":[158],"until":[159],"no":[160],"further":[161],"combinations":[162],"possible,":[164],"resulting":[165],"refined":[168],"shape":[170,182],"tree.":[171],"obtain":[173],"desired":[175],"abstraction,":[177],"filter":[179],"by":[184],"examining":[185],"factors":[186],"such":[187],"as":[188],"differences,":[190],"relative":[191],"sizes,":[192],"layering":[195],"within":[196],"hierarchy":[198],"region":[201],"relation":[203],"parent":[206],"regions.":[207],"By":[208],"employing":[209],"this":[210],"approach,":[211],"can":[213],"preserve":[214],"apply":[217],"local":[218],"filtering":[219],"operations,":[220],"effectively":[222],"shapes.":[227],"results":[229],"well-suited":[233],"applications":[237],"artistic":[239],"renderings.":[240]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}