{"id":"https://openalex.org/W2134484150","doi":"https://doi.org/10.1109/cvpr.2011.5995322","title":"Sparse shape composition: A new framework for shape prior modeling","display_name":"Sparse shape composition: A new framework for shape prior modeling","publication_year":2011,"publication_date":"2011-06-01","ids":{"openalex":"https://openalex.org/W2134484150","doi":"https://doi.org/10.1109/cvpr.2011.5995322","mag":"2134484150"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2011.5995322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2011.5995322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CVPR 2011","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/A5066553616","display_name":"Shaoting Zhang","orcid":"https://orcid.org/0000-0002-8719-448X"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]},{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shaoting Zhang","raw_affiliation_strings":["CAD Research and Development, Siemens Healthcare, Malvern, PA, USA","Department of Computer Science, Rutgers University, Piscataway, NJ, USA"],"affiliations":[{"raw_affiliation_string":"CAD Research and Development, Siemens Healthcare, Malvern, PA, USA","institution_ids":["https://openalex.org/I4210151799"]},{"raw_affiliation_string":"Department of Computer Science, Rutgers University, Piscataway, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062819602","display_name":"Yiqiang Zhan","orcid":"https://orcid.org/0000-0001-8391-2555"},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiqiang Zhan","raw_affiliation_strings":["CAD Research and Development, Siemens Healthcare, Malvern, PA, USA"],"affiliations":[{"raw_affiliation_string":"CAD Research and Development, Siemens Healthcare, Malvern, PA, USA","institution_ids":["https://openalex.org/I4210151799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089476507","display_name":"M. Ali Akber Dewan","orcid":"https://orcid.org/0000-0001-6347-7509"},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maneesh Dewan","raw_affiliation_strings":["CAD Research and Development, Siemens Healthcare, Malvern, PA, USA"],"affiliations":[{"raw_affiliation_string":"CAD Research and Development, Siemens Healthcare, Malvern, PA, USA","institution_ids":["https://openalex.org/I4210151799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068865316","display_name":"Junzhou Huang","orcid":"https://orcid.org/0000-0002-9548-1227"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junzhou Huang","raw_affiliation_strings":["Department of Computer Science, Rutgers University, Piscataway, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rutgers University, Piscataway, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109600054","display_name":"Dimitris Metaxas","orcid":null},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dimitris N. Metaxas","raw_affiliation_strings":["Department of Computer Science, Rutgers University, Piscataway, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rutgers University, Piscataway, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103008068","display_name":"Xiang Sean Zhou","orcid":"https://orcid.org/0000-0002-4907-249X"},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Sean Zhou","raw_affiliation_strings":["CAD Research and Development, Siemens Healthcare, Malvern, PA, USA"],"affiliations":[{"raw_affiliation_string":"CAD Research and Development, Siemens Healthcare, Malvern, PA, USA","institution_ids":["https://openalex.org/I4210151799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5066553616"],"corresponding_institution_ids":["https://openalex.org/I102322142","https://openalex.org/I4210151799"],"apc_list":null,"apc_paid":null,"fwci":6.2781,"has_fulltext":false,"cited_by_count":55,"citation_normalized_percentile":{"value":0.97247536,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1025","last_page":"1032"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.998199999332428,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7218326330184937},{"id":"https://openalex.org/keywords/active-shape-model","display_name":"Active shape model","score":0.7175419926643372},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6120216250419617},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6086374521255493},{"id":"https://openalex.org/keywords/shape-analysis","display_name":"Shape analysis (program analysis)","score":0.5805431604385376},{"id":"https://openalex.org/keywords/active-appearance-model","display_name":"Active appearance model","score":0.47920116782188416},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4614933729171753},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4530101716518402},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4486909508705139},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.44852322340011597},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.44738203287124634},{"id":"https://openalex.org/keywords/point-distribution-model","display_name":"Point distribution model","score":0.4423861801624298},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39418959617614746},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3023275136947632},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.23470112681388855}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7218326330184937},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.7175419926643372},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6120216250419617},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6086374521255493},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.5805431604385376},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.47920116782188416},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4614933729171753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4530101716518402},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4486909508705139},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.44852322340011597},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.44738203287124634},{"id":"https://openalex.org/C118317068","wikidata":"https://www.wikidata.org/wiki/Q2100760","display_name":"Point distribution model","level":2,"score":0.4423861801624298},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39418959617614746},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3023275136947632},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.23470112681388855},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C97686452","wikidata":"https://www.wikidata.org/wiki/Q7604153","display_name":"Static analysis","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2011.5995322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2011.5995322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CVPR 2011","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.295.4638","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.4638","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.research.rutgers.edu/~shaoting/paper/CVPR11.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309549","display_name":"University of Houston","ror":"https://ror.org/040vwpm13"},{"id":"https://openalex.org/F4320310009","display_name":"University of Florida","ror":"https://ror.org/02y3ad647"},{"id":"https://openalex.org/F4320310365","display_name":"Lehigh University","ror":"https://ror.org/012afjb06"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W33507944","https://openalex.org/W1485462945","https://openalex.org/W1499329969","https://openalex.org/W1576409155","https://openalex.org/W1994830509","https://openalex.org/W2025051526","https://openalex.org/W2038952578","https://openalex.org/W2082541576","https://openalex.org/W2085261163","https://openalex.org/W2104095591","https://openalex.org/W2104276184","https://openalex.org/W2105789358","https://openalex.org/W2114113040","https://openalex.org/W2115458614","https://openalex.org/W2128409098","https://openalex.org/W2129638195","https://openalex.org/W2129812935","https://openalex.org/W2141075465","https://openalex.org/W2141453390","https://openalex.org/W2142152601","https://openalex.org/W2154996879","https://openalex.org/W2157898655","https://openalex.org/W2164867175","https://openalex.org/W2167825066","https://openalex.org/W2171490473","https://openalex.org/W2538773003","https://openalex.org/W2544981553","https://openalex.org/W3158522210","https://openalex.org/W6629098971","https://openalex.org/W6629740406","https://openalex.org/W6675717728","https://openalex.org/W6680805102","https://openalex.org/W6682856636","https://openalex.org/W6728784960"],"related_works":["https://openalex.org/W2126962853","https://openalex.org/W2464585191","https://openalex.org/W2167243075","https://openalex.org/W2532906573","https://openalex.org/W108291172","https://openalex.org/W2121756800","https://openalex.org/W1990603712","https://openalex.org/W2250899144","https://openalex.org/W2101489611","https://openalex.org/W771549137"],"abstract_inverted_index":{"Image":[0],"appearance":[1,25,44,76],"cues":[2,26,77],"are":[3,27,92,98,133,189],"often":[4],"used":[5],"to":[6,35,94,118,136,240],"derive":[7],"object":[8],"shapes,":[9],"which":[10,205],"is":[11,51,57,144,198,206,216],"usually":[12,190],"one":[13],"of":[14,18,48,88,155,173,178],"the":[15,39,89,103,156,161,179],"key":[16],"steps":[17],"image":[19,24,75],"understanding":[20],"tasks.":[21],"However,":[22],"when":[23],"weak":[28],"or":[29],"misleading,":[30],"shape":[31,40,49,55,71,91,158,163,181],"priors":[32,50],"become":[33],"critical":[34],"infer":[36],"and":[37,59,84,231],"refine":[38],"derived":[41,73],"by":[42,64,168,208],"these":[43,121],"cues.":[45],"Effective":[46],"modeling":[47],"challenging":[52],"because:":[53],"1)":[54,160],"variation":[56],"complex":[58],"cannot":[60],"always":[61],"be":[62,165],"modeled":[63],"a":[65,70,111,125,169,201],"parametric":[66],"probability":[67],"distribution;":[68],"2)":[69,176],"instance":[72],"from":[74],"(input":[78],"shape)":[79],"may":[80,182],"have":[81],"gross":[82,184],"errors;":[83],"3)":[85],"local":[86],"details":[87],"input":[90,139,157,162,180],"difficult":[93],"preserve":[95],"if":[96],"they":[97],"not":[99],"statistically":[100],"significant":[101],"in":[102,124,228,235,248],"training":[104,131,174],"data.":[105],"In":[106,128],"this":[107],"paper":[108],"we":[109],"propose":[110],"novel":[112],"Sparse":[113],"Shape":[114],"Composition":[115],"model":[116,150,197,244],"(SSC)":[117],"deal":[119],"with":[120],"three":[122],"challenges":[123],"unified":[126],"framework.":[127,213],"our":[129,196,243],"method,":[130],"shapes":[132],"adaptively":[134],"composed":[135],"infer/refine":[137],"an":[138,209],"shape.":[140],"The":[141],"a-priori":[142],"information":[143],"thus":[145],"implicitly":[146],"incorporated":[147],"on-the-fly.":[148],"Our":[149,214],"leverages":[151],"two":[152,220],"sparsity":[153],"observations":[154],"instance:":[159],"can":[164],"approximately":[166],"represented":[167],"sparse":[170],"linear":[171],"combination":[172],"shapes;":[175],"parts":[177],"contain":[183],"errors":[185,188],"but":[186],"such":[187],"sparse.":[191],"Using":[192],"L1":[193],"norm":[194],"relaxation,":[195],"formulated":[199],"as":[200],"convex":[202],"optimization":[203],"problem,":[204],"solved":[207],"efficient":[210],"alternating":[211],"minimization":[212],"method":[215],"extensively":[217],"validated":[218],"on":[219],"real":[221],"world":[222],"medical":[223],"applications,":[224],"2D":[225],"lung":[226],"localization":[227],"X-ray":[229],"images":[230],"3D":[232],"liver":[233],"segmentation":[234],"low-dose":[236],"CT":[237],"scans.":[238],"Compared":[239],"state-of-the-art":[241],"methods,":[242],"exhibits":[245],"better":[246],"performance":[247],"both":[249],"studies.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":10},{"year":2013,"cited_by_count":12},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
