{"id":"https://openalex.org/W3171589731","doi":"https://doi.org/10.1109/ieeeconf51394.2020.9443562","title":"Steerable Pyramid for Texture Classification of Photographic Paper","display_name":"Steerable Pyramid for Texture Classification of Photographic Paper","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3171589731","doi":"https://doi.org/10.1109/ieeeconf51394.2020.9443562","mag":"3171589731"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf51394.2020.9443562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf51394.2020.9443562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","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/A5002069854","display_name":"Nicholas Rogers","orcid":"https://orcid.org/0000-0002-0758-8439"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nicholas Rogers","raw_affiliation_strings":["Yale University,IPCH Lens Media Lab,New Haven,CT,USA","IPCH Lens Media Lab, Yale University, New Haven, CT, USA"],"affiliations":[{"raw_affiliation_string":"Yale University,IPCH Lens Media Lab,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"IPCH Lens Media Lab, Yale University, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030037732","display_name":"Damon Crockett","orcid":"https://orcid.org/0000-0002-6674-3013"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Damon Crockett","raw_affiliation_strings":["Yale University,IPCH Lens Media Lab,New Haven,CT,USA","IPCH Lens Media Lab, Yale University, New Haven, CT, USA"],"affiliations":[{"raw_affiliation_string":"Yale University,IPCH Lens Media Lab,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"IPCH Lens Media Lab, Yale University, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032167214","display_name":"Paul Messier","orcid":"https://orcid.org/0000-0001-5612-1483"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paul Messier","raw_affiliation_strings":["Yale University,IPCH Lens Media Lab,New Haven,CT,USA","IPCH Lens Media Lab, Yale University, New Haven, CT, USA"],"affiliations":[{"raw_affiliation_string":"Yale University,IPCH Lens Media Lab,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"IPCH Lens Media Lab, Yale University, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002069854"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.33061606,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"132","last_page":"136"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9950000047683716,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9909999966621399,"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/texture","display_name":"Texture (cosmology)","score":0.652328610420227},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.64478600025177},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6225030422210693},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.603282630443573},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5956177711486816},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.5013024806976318},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40385568141937256},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3432813286781311},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2277926206588745},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21097847819328308},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.11305272579193115},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05814680457115173}],"concepts":[{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.652328610420227},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.64478600025177},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6225030422210693},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.603282630443573},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5956177711486816},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.5013024806976318},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40385568141937256},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3432813286781311},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2277926206588745},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21097847819328308},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.11305272579193115},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05814680457115173}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf51394.2020.9443562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf51394.2020.9443562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W2037603075","https://openalex.org/W2107790757","https://openalex.org/W2109812093","https://openalex.org/W2118642595","https://openalex.org/W2127006916","https://openalex.org/W2294894928","https://openalex.org/W2532775160","https://openalex.org/W2594190411","https://openalex.org/W2890977309"],"related_works":["https://openalex.org/W4249847449","https://openalex.org/W44395729","https://openalex.org/W2765338038","https://openalex.org/W2044270176","https://openalex.org/W2374828682","https://openalex.org/W2151022383","https://openalex.org/W2153116791","https://openalex.org/W2388733570","https://openalex.org/W4230530180","https://openalex.org/W1980033651"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,144,162],"describe":[4,145],"the":[5,8,20,46,83,95,164],"application":[6],"of":[7,24,28,36,48,73,86,98,120,148,158],"\u2018steerable":[9],"pyramid\u2019":[10],"(Portilla":[11],"and":[12,22,51,59,77,150],"Simoncelli,":[13],"2000),":[14],"a":[15,25,37,130,137,146,156],"high-dimensional":[16],"texture":[17],"descriptor,":[18],"to":[19,40,45,68,78,154],"discrimination":[21],"classification":[23,173],"large":[26],"collection":[27],"photographic":[29,49,87],"paper":[30,184],"textures.":[31],"This":[32],"work":[33],"is":[34,65,104,112],"part":[35],"broader":[38],"effort":[39],"bring":[41],"precise":[42,117],"physical":[43],"measurements":[44],"characterization":[47],"papers,":[50],"our":[52,62],"intended":[53],"audience":[54],"includes":[55,180],"photograph":[56],"conservators,":[57],"curators,":[58],"collectors.":[60],"For":[61,141],"purposes,":[63],"it":[64,103,127],"important":[66],"both":[67],"enable":[69],"fine-grained":[70],"local":[71,118],"comparisons":[72,119],"very":[74],"similar":[75],"textures":[76],"identify":[79],"salient":[80],"groupings":[81],"across":[82],"entire":[84],"landscape":[85],"papers.":[88],"The":[89],"steerable":[90,165],"pyramid":[91,166],"was":[92],"developed":[93],"with":[94],"explicit":[96],"goal":[97],"synthesizing":[99],"textures;":[100],"as":[101,122],"such,":[102],"an":[105,113,175],"overcomplete":[106],"representation.":[107],"Its":[108],"high":[109],"native":[110],"dimensionality":[111],"advantage":[114],"for":[115,132],"highly":[116],"texture,":[121],"in":[123],"nearest":[124],"neighbor":[125],"search;":[126],"is,":[128],"however,":[129],"disadvantage":[131],"grouping":[133,142,151],"tasks,":[134,143],"which":[135],"require":[136],"well-structured":[138],"global":[139],"topology.":[140],"number":[147],"compression":[149],"approaches":[152],"designed":[153],"meet":[155],"variety":[157],"analytical":[159],"needs.":[160],"Finally,":[161],"validate":[163],"descriptor":[167],"numerically":[168],"using":[169],"\u2018one":[170],"versus":[171],"all\u2019":[172],"on":[174],"augmented":[176],"data":[177],"set":[178],"that":[179],"multiple":[181],"samples":[182],"per":[183],"type.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
