{"id":"https://openalex.org/W6926300308","doi":"https://doi.org/10.2312/3dor/3dor11/049-056","title":"Local Shape Descriptors, a Survey and Evaluation","display_name":"Local Shape Descriptors, a Survey and Evaluation","publication_year":2011,"publication_date":"2011-01-01","ids":{"openalex":"https://openalex.org/W6926300308","doi":"https://doi.org/10.2312/3dor/3dor11/049-056"},"language":"en","primary_location":{"id":"doi:10.2312/3dor/3dor11/049-056","is_oa":true,"landing_page_url":"https://doi.org/10.2312/3dor/3dor11/049-056","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/3dor/3dor11/049-056","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Heider, Paul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heider, Paul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Pierre-Pierre, Alain","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pierre-Pierre, Alain","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Li, Ruosi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ruosi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Grimm, Cindy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grimm, Cindy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10410","display_name":"COVID-19 epidemiological studies","score":0.016599999740719795,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"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/T10410","display_name":"COVID-19 epidemiological studies","score":0.016599999740719795,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"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/T14186","display_name":"Healthcare Systems and Practices","score":0.013399999588727951,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12943","display_name":"COVID-19 Digital Contact Tracing","score":0.012500000186264515,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/robustness","display_name":"Robustness (evolution)","score":0.6599000096321106},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.595300018787384},{"id":"https://openalex.org/keywords/shape-analysis","display_name":"Shape analysis (program analysis)","score":0.583299994468689},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.527999997138977},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5127000212669373},{"id":"https://openalex.org/keywords/point-distribution-model","display_name":"Point distribution model","score":0.42969998717308044},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.40880000591278076}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6599000096321106},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6489999890327454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.595300018787384},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.583299994468689},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.527999997138977},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5127000212669373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44850000739097595},{"id":"https://openalex.org/C118317068","wikidata":"https://www.wikidata.org/wiki/Q2100760","display_name":"Point distribution model","level":2,"score":0.42969998717308044},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42969998717308044},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.40880000591278076},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.3806999921798706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3668000102043152},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3357999920845032},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C2778585274","wikidata":"https://www.wikidata.org/wiki/Q2845240","display_name":"Procrustes analysis","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29919999837875366},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C45089102","wikidata":"https://www.wikidata.org/wiki/Q5693286","display_name":"Heat kernel signature","level":4,"score":0.26499998569488525}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2312/3dor/3dor11/049-056","is_oa":true,"landing_page_url":"https://doi.org/10.2312/3dor/3dor11/049-056","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.2312/3dor/3dor11/049-056","is_oa":true,"landing_page_url":"https://doi.org/10.2312/3dor/3dor11/049-056","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7561655640602112,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Local":[0],"shape":[1,16,27,106,141],"descriptors":[2,28,53,67,101],"can":[3],"be":[4],"used":[5],"for":[6,30,57,102],"a":[7,23,49,55,62,73],"variety":[8,24,74],"of":[9,25,51,65,75,99,105],"tasks,":[10,32],"from":[11,72],"registration":[12],"to":[13,15,127,136],"comparison":[14],"analysis":[17],"and":[18,54,91,116,123,138],"retrieval.":[19],"There":[20],"have":[21,34],"been":[22,35],"local":[26],"developed":[29],"these":[31,82],"which":[33],"evaluated":[36],"in":[37,40,87],"isolation":[38],"or":[39],"pairs,":[41],"but":[42],"not":[43],"against":[44],"each":[45],"other.":[46],"We":[47,60,77,93],"provide":[48],"survey":[50],"existing":[52],"framework":[56],"comparing":[58],"them.":[59],"perform":[61],"detailed":[63],"evaluation":[64],"the":[66,96,100,103,113,117,139],"using":[68,120],"real":[69],"data":[70,126],"sets":[71],"sources.":[76],"first":[78],"evaluate":[79],"how":[80],"stable":[81],"metrics":[83],"are":[84],"under":[85],"changes":[86],"mesh":[88],"resolution,":[89],"noise,":[90],"smoothing.":[92],"then":[94],"analyze":[95],"discriminatory":[97],"ability":[98],"task":[104],"matching.":[107],"Our":[108],"conclusion":[109],"is":[110],"that":[111],"sampling":[112],"normal":[114],"distribution":[115],"mean":[118],"curvature,":[119],"25":[121],"samples,":[122],"reducing":[124],"this":[125],"5-10":[128],"samples":[129],"via":[130],"Principal":[131],"Components":[132],"Analysis":[133],"provides":[134],"robustness":[135],"noise":[137],"best":[140],"discrimination":[142],"results.":[143]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
