{"id":"https://openalex.org/W2171117728","doi":"https://doi.org/10.1109/icassp.2004.1326597","title":"Application of time series techniques to data mining and analysis of spatial patterns in 3D images","display_name":"Application of time series techniques to data mining and analysis of spatial patterns in 3D images","publication_year":2004,"publication_date":"2004-09-28","ids":{"openalex":"https://openalex.org/W2171117728","doi":"https://doi.org/10.1109/icassp.2004.1326597","mag":"2171117728"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2004.1326597","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2004.1326597","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE International Conference on Acoustics, Speech, and Signal Processing","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/A5100709541","display_name":"Qiang Wang","orcid":"https://orcid.org/0000-0001-5632-4408"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qiang Wang","raw_affiliation_strings":["Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060899856","display_name":"Despina Kontos","orcid":"https://orcid.org/0000-0001-9031-5126"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"D. Kontos","raw_affiliation_strings":["Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101740853","display_name":"Guo Li","orcid":"https://orcid.org/0000-0002-4547-0896"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guo Li","raw_affiliation_strings":["Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076170742","display_name":"V. Megalooikonomou","orcid":null},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"V. Megalooikonomou","raw_affiliation_strings":["Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100709541"],"corresponding_institution_ids":["https://openalex.org/I84392919"],"apc_list":null,"apc_paid":null,"fwci":1.6826,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.85330601,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"3","issue":null,"first_page":"iii","last_page":"525"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9668999910354614,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.9153000116348267,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/discriminative-model","display_name":"Discriminative model","score":0.7087674140930176},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6563801765441895},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6252456903457642},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6154463291168213},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5512903928756714},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5480189919471741},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5447061061859131},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5263508558273315},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5166082978248596},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5121333599090576},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.5030161738395691},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5021235942840576},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.48219504952430725},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.47024765610694885},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4453841745853424},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3177483081817627},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2525109350681305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22189638018608093},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11539936065673828}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7087674140930176},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6563801765441895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6252456903457642},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6154463291168213},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5512903928756714},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5480189919471741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5447061061859131},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5263508558273315},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5166082978248596},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5121333599090576},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.5030161738395691},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5021235942840576},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.48219504952430725},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.47024765610694885},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4453841745853424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3177483081817627},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2525109350681305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22189638018608093},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11539936065673828},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2004.1326597","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2004.1326597","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE International Conference on Acoustics, Speech, and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W30647502","https://openalex.org/W1499049447","https://openalex.org/W1963737619","https://openalex.org/W2001103857","https://openalex.org/W2003220467","https://openalex.org/W2066796814","https://openalex.org/W2072605585","https://openalex.org/W2116649573","https://openalex.org/W2128061541","https://openalex.org/W2140235447","https://openalex.org/W2159772324","https://openalex.org/W2163336863","https://openalex.org/W4236139033"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2120164251"],"abstract_inverted_index":{"Analysis":[0],"of":[1,62,65,75,101],"spatial":[2,34,55,103,120],"patterns":[3,35,51,121],"in":[4,10,16,57],"images":[5],"can":[6],"provide":[7],"valuable":[8],"information":[9],"many":[11],"application":[12],"domains,":[13],"such":[14,89],"as":[15,90,142,144],"geography,":[17],"meteorology":[18],"and":[19,85,94,129,135,141],"medicine.":[20],"We":[21],"propose":[22,72],"to":[23,31],"apply":[24],"techniques":[25],"from":[26,37],"the":[27,33,54,58,63,66,73,102,118,130],"time":[28,69,77],"series":[29,78],"domain":[30],"analyze":[32,110],"extracted":[36],"3D":[38],"images.":[39],"After":[40],"traversing":[41],"an":[42,111],"image":[43],"using":[44],"a":[45,106],"space-filling":[46],"curve,":[47],"we":[48,71,109],"discover":[49],"discriminative":[50,124],"by":[52],"analyzing":[53],"sequence":[56],"transformed":[59],"domain.":[60],"Because":[61],"similarity":[64,79,136],"sequences":[67],"with":[68],"series,":[70],"use":[74],"existing":[76],"analysis":[80,100],"techniques,":[81,88],"including":[82],"Euclidean":[83],"distance,":[84],"dimensionality":[86],"reduction":[87],"singular":[91],"value":[92],"decomposition":[93],"piecewise":[95],"aggregate":[96],"approximation,":[97],"for":[98,133,146],"further":[99],"patterns.":[104],"As":[105],"case":[107],"study,":[108],"fMRI":[112],"dataset.":[113],"Experimental":[114],"results":[115],"verify":[116],"that":[117],"discovered":[119],"have":[122],"strong":[123],"power":[125],"among":[126],"different":[127],"classes":[128],"overall":[131],"accuracy":[132],"clustering":[134],"retrieval":[137],"is":[138],"above":[139],"90%":[140],"high":[143],"100%":[145],"certain":[147],"experimental":[148],"settings.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2013,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
