{"id":"https://openalex.org/W2014608344","doi":"https://doi.org/10.1145/1273496.1273574","title":"Linear and nonlinear generative probabilistic class models for shape contours","display_name":"Linear and nonlinear generative probabilistic class models for shape contours","publication_year":2007,"publication_date":"2007-06-20","ids":{"openalex":"https://openalex.org/W2014608344","doi":"https://doi.org/10.1145/1273496.1273574","mag":"2014608344"},"language":"en","primary_location":{"id":"doi:10.1145/1273496.1273574","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1273496.1273574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th international conference on Machine learning","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://era.ed.ac.uk/bitstream/1842/3703/1/mcneill-ICML2007.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037244349","display_name":"Graham McNeill","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Graham McNeill","raw_affiliation_strings":["University of Edinburgh, Edinburgh, UK","University of Edinburgh, Edinburgh , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"University of Edinburgh, Edinburgh , UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069715982","display_name":"Sethu Vijayakumar","orcid":"https://orcid.org/0000-0003-0649-7241"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sethu Vijayakumar","raw_affiliation_strings":["University of Edinburgh, Edinburgh, UK","University of Edinburgh, Edinburgh , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"University of Edinburgh, Edinburgh , UK","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.10416667,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"617","last_page":"624"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9871000051498413,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.6597653031349182},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5977441072463989},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5835208892822266},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5678013563156128},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.5334743857383728},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5030903220176697},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5010912418365479},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47201627492904663},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.44038230180740356},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4202157258987427},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4179636240005493},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.36966434121131897},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3669108748435974},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35700514912605286},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.28959277272224426},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17077606916427612}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6597653031349182},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5977441072463989},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5835208892822266},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5678013563156128},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.5334743857383728},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5030903220176697},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5010912418365479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47201627492904663},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.44038230180740356},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4202157258987427},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4179636240005493},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.36966434121131897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3669108748435974},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35700514912605286},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.28959277272224426},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17077606916427612},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/1273496.1273574","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1273496.1273574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th international conference on Machine learning","raw_type":"proceedings-article"},{"id":"pmh:oai:era.ed.ac.uk:1842/3703","is_oa":true,"landing_page_url":"http://hdl.handle.net/1842/3703","pdf_url":"https://era.ed.ac.uk/bitstream/1842/3703/1/mcneill-ICML2007.pdf","source":{"id":"https://openalex.org/S7407055182","display_name":"ERA","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.63.6989","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.6989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://homepages.inf.ed.ac.uk/svijayak/publications/mcneill-ICML2007.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.77.9857","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.9857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://imls.engr.oregonstate.edu/www/htdocs/proceedings/icml2007/papers/371.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:era.ed.ac.uk:1842/3703","is_oa":true,"landing_page_url":"http://hdl.handle.net/1842/3703","pdf_url":"https://era.ed.ac.uk/bitstream/1842/3703/1/mcneill-ICML2007.pdf","source":{"id":"https://openalex.org/S7407055182","display_name":"ERA","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2014608344.pdf","grobid_xml":"https://content.openalex.org/works/W2014608344.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1506806321","https://openalex.org/W1663973292","https://openalex.org/W1980812303","https://openalex.org/W2034627659","https://openalex.org/W2054886856","https://openalex.org/W2056615005","https://openalex.org/W2059804132","https://openalex.org/W2082541576","https://openalex.org/W2099530880","https://openalex.org/W2114766304","https://openalex.org/W2117564239","https://openalex.org/W2122870750","https://openalex.org/W2153675946","https://openalex.org/W2156851564","https://openalex.org/W2159479372","https://openalex.org/W2594639291","https://openalex.org/W3021971632","https://openalex.org/W4292156489","https://openalex.org/W6629510986"],"related_works":["https://openalex.org/W2461917396","https://openalex.org/W2037497866","https://openalex.org/W4243467573","https://openalex.org/W1502435251","https://openalex.org/W62001224","https://openalex.org/W3032390039","https://openalex.org/W1584341211","https://openalex.org/W2616125534","https://openalex.org/W2963987720","https://openalex.org/W2146310005"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,12,61,123],"robust":[3],"probabilistic":[4],"approach":[5],"to":[6,20,37,47,66,77,103],"modeling":[7],"shape":[8,40,49],"contours":[9,57],"based":[10],"on":[11,118],"lowdimensional,":[13],"nonlinear":[14],"latent":[15],"variable":[16],"model.":[17],"In":[18,70],"contrast":[19],"existing":[21],"techniques":[22],"that":[23],"use":[24,95],"objective":[25],"functions":[26,99],"in":[27],"data":[28,53,81,120],"space":[29],"without":[30],"explicit":[31],"noise":[32],"models,":[33],"we":[34],"are":[35,75,92,101],"able":[36],"extract":[38],"complex":[39],"variation":[41],"from":[42],"noisy":[43],"data.":[44],"Most":[45],"approaches":[46],"learning":[48],"models":[50],"slide":[51],"observed":[52],"points":[54],"around":[55],"fixed":[56,80],"and":[58,100,106,122],"hence,":[59],"require":[60],"correctly":[62],"labeled":[63],"'reference":[64],"shape'":[65],"prevent":[67],"degenerate":[68],"solutions.":[69],"our":[71],"method,":[72],"unobserved":[73],"curves":[74],"reparameterized":[76],"explain":[78],"the":[79],"points,":[82],"so":[83],"this":[84],"problem":[85],"does":[86],"not":[87],"arise.":[88],"The":[89],"proposed":[90],"algorithms":[91],"suitable":[93],"for":[94],"with":[96],"arbitrary":[97],"basis":[98],"applicable":[102],"both":[104],"open":[105],"closed":[107],"shapes;":[108],"their":[109],"effectiveness":[110],"is":[111],"demonstrated":[112],"through":[113],"illustrative":[114],"examples,":[115],"quantitative":[116],"assessment":[117],"benchmark":[119],"sets":[121],"visualization":[124],"task.":[125]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-08-02T14:50:37.381335","created_date":"2025-10-10T00:00:00"}