A Bayesian Formulation of Coherent Point Drift

Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as point sets. Despite its advantages over other state-of-the-art algorithms, theoretical and practical issues remain. Among theoretical issues, (...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 43; číslo 7; s. 2269 - 2286
Hlavný autor:	Hirose, Osamu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Bayes methods Bayesian analysis Coherence coherent point drift Convergence Drift fast computation Inference algorithms Kernel Kernels Matrix converters motion coherence Non-rigid point set registration Parameters Rotation Shape Statistical inference Three-dimensional displays variational Bayesian inference
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as point sets. Despite its advantages over other state-of-the-art algorithms, theoretical and practical issues remain. Among theoretical issues, (1) it is unknown whether the algorithm always converges, and (2) the meaning of the parameters concerning motion coherence is unclear. Among practical issues, (3) the algorithm is relatively sensitive to target shape rotation, and (4) acceleration of the algorithm is restricted to the use of the Gaussian kernel. To overcome these issues and provide a different and more general perspective to the algorithm, we formulate coherent point drift in a Bayesian setting. The formulation brings the following consequences and advances to the field: convergence of the algorithm is guaranteed by variational Bayesian inference; the definition of motion coherence as a prior distribution provides a basis for interpretation of the parameters; rigid and non-rigid registration can be performed in a single algorithm, enhancing robustness against target rotation. We also propose an acceleration scheme for the algorithm that can be applied to non-Gaussian kernels and that provides greater efficiency than coherent point drift.
AbstractList	Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as point sets. Despite its advantages over other state-of-the-art algorithms, theoretical and practical issues remain. Among theoretical issues, (1) it is unknown whether the algorithm always converges, and (2) the meaning of the parameters concerning motion coherence is unclear. Among practical issues, (3) the algorithm is relatively sensitive to target shape rotation, and (4) acceleration of the algorithm is restricted to the use of the Gaussian kernel. To overcome these issues and provide a different and more general perspective to the algorithm, we formulate coherent point drift in a Bayesian setting. The formulation brings the following consequences and advances to the field: convergence of the algorithm is guaranteed by variational Bayesian inference; the definition of motion coherence as a prior distribution provides a basis for interpretation of the parameters; rigid and non-rigid registration can be performed in a single algorithm, enhancing robustness against target rotation. We also propose an acceleration scheme for the algorithm that can be applied to non-Gaussian kernels and that provides greater efficiency than coherent point drift. Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as point sets. Despite its advantages over other state-of-the-art algorithms, theoretical and practical issues remain. Among theoretical issues, (1) it is unknown whether the algorithm always converges, and (2) the meaning of the parameters concerning motion coherence is unclear. Among practical issues, (3) the algorithm is relatively sensitive to target shape rotation, and (4) acceleration of the algorithm is restricted to the use of the Gaussian kernel. To overcome these issues and provide a different and more general perspective to the algorithm, we formulate coherent point drift in a Bayesian setting. The formulation brings the following consequences and advances to the field: convergence of the algorithm is guaranteed by variational Bayesian inference; the definition of motion coherence as a prior distribution provides a basis for interpretation of the parameters; rigid and non-rigid registration can be performed in a single algorithm, enhancing robustness against target rotation. We also propose an acceleration scheme for the algorithm that can be applied to non-Gaussian kernels and that provides greater efficiency than coherent point drift.Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as point sets. Despite its advantages over other state-of-the-art algorithms, theoretical and practical issues remain. Among theoretical issues, (1) it is unknown whether the algorithm always converges, and (2) the meaning of the parameters concerning motion coherence is unclear. Among practical issues, (3) the algorithm is relatively sensitive to target shape rotation, and (4) acceleration of the algorithm is restricted to the use of the Gaussian kernel. To overcome these issues and provide a different and more general perspective to the algorithm, we formulate coherent point drift in a Bayesian setting. The formulation brings the following consequences and advances to the field: convergence of the algorithm is guaranteed by variational Bayesian inference; the definition of motion coherence as a prior distribution provides a basis for interpretation of the parameters; rigid and non-rigid registration can be performed in a single algorithm, enhancing robustness against target rotation. We also propose an acceleration scheme for the algorithm that can be applied to non-Gaussian kernels and that provides greater efficiency than coherent point drift.
Author	Hirose, Osamu
Author_xml	– sequence: 1 givenname: Osamu orcidid: 0000-0002-8077-8589 surname: Hirose fullname: Hirose, Osamu email: hirose@se.kanazawa-u.ac.jp organization: Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Ishikawa, Japan
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32031931$$D View this record in MEDLINE/PubMed
BookMark	eNp9kDtPwzAURi0EgvL4AyChSCwsKdfXiWuPpTwlEAwwW056LYzSGOxk6L8npYWBgcVezvl0dfbZdhtaYuyYw5hz0Bcvz9PH-zECwhj1hEs12WIj5BJyjRq32Qi4xFwpVHtsP6V3AF6UIHbZnkAQXAs-YuNpdmmXlLxts5sQF31jOx_aLLhsFt4oUttlz8EP71X0rjtkO842iY42_wF7vbl-md3lD0-397PpQ14Xkne5cKDQYuEElPVc6LqwVTUnISuJldbEsZzgcAFITppDqSprCbkDx5Wr5qU4YOfr3Y8YPntKnVn4VFPT2JZCnwyKEmUhC6EG9OwP-h762A7XGSyFHsBCrwZPN1RfLWhuPqJf2Lg0PyUGQK2BOoaUIjlT--67RRetbwwHs4puvqObVXSziT6o-Ef9Wf9XOllLnoh-BaVVKWAivgDgjIm-
CODEN	ITPIDJ
CitedBy_id	crossref_primary_10_1109_TIM_2022_3169559 crossref_primary_10_1109_TMECH_2024_3390225 crossref_primary_10_1109_ACCESS_2022_3192869 crossref_primary_10_1109_TPAMI_2023_3247603 crossref_primary_10_32604_cmes_2023_025662 crossref_primary_10_1109_LRA_2025_3539105 crossref_primary_10_1108_RIA_04_2022_0083 crossref_primary_10_1109_TPAMI_2020_3043769 crossref_primary_10_1109_JTEHM_2021_3056618 crossref_primary_10_1109_TMI_2024_3457228 crossref_primary_10_3389_fbioe_2023_1244291 crossref_primary_10_1016_j_neucom_2021_08_080 crossref_primary_10_1007_s00371_025_03914_9 crossref_primary_10_1038_s41592_025_02794_0 crossref_primary_10_1016_j_measurement_2023_112807 crossref_primary_10_1109_TPAMI_2025_3572795 crossref_primary_10_1371_journal_pone_0299040 crossref_primary_10_1109_TASE_2022_3159553 crossref_primary_10_1126_scirobotics_abe1315 crossref_primary_10_1007_s13042_022_01673_w crossref_primary_10_1145_3528223_3530116 crossref_primary_10_1007_s11042_022_14250_8 crossref_primary_10_1016_j_srs_2024_100194 crossref_primary_10_1038_s41597_025_04467_1 crossref_primary_10_1088_1361_6501_ad2b42 crossref_primary_10_1007_s11548_023_02915_0 crossref_primary_10_1145_3569091 crossref_primary_10_1109_TIP_2022_3148822 crossref_primary_10_1109_TIP_2022_3223793 crossref_primary_10_1109_TPAMI_2022_3214191 crossref_primary_10_1016_j_rcim_2024_102772 crossref_primary_10_1109_TAES_2024_3381925 crossref_primary_10_1016_j_compbiomed_2023_106806 crossref_primary_10_1109_TIM_2024_3470951 crossref_primary_10_1111_cgf_14502 crossref_primary_10_1111_cgf_14788 crossref_primary_10_1109_ACCESS_2021_3111811 crossref_primary_10_1007_s13246_023_01245_4 crossref_primary_10_1038_s41467_024_45861_4 crossref_primary_10_1007_s00371_021_02335_8 crossref_primary_10_1109_TMI_2024_3413537 crossref_primary_10_3390_jimaging9090179 crossref_primary_10_3390_s24216924 crossref_primary_10_1007_s10462_022_10292_4 crossref_primary_10_1109_TPAMI_2023_3262780 crossref_primary_10_1016_j_cma_2024_117401 crossref_primary_10_1016_j_cad_2025_103888 crossref_primary_10_1109_TFUZZ_2022_3159099 crossref_primary_10_1109_TMECH_2023_3260966 crossref_primary_10_2514_1_G007337 crossref_primary_10_1109_ACCESS_2024_3519671 crossref_primary_10_1016_j_patcog_2022_109124 crossref_primary_10_1007_s11042_023_14776_5 crossref_primary_10_3389_fbioe_2024_1384599 crossref_primary_10_1016_j_compbiomed_2021_105125 crossref_primary_10_1016_j_autcon_2023_104907 crossref_primary_10_1109_JSEN_2024_3471651 crossref_primary_10_1109_LRA_2021_3093011 crossref_primary_10_1109_TVCG_2023_3283990 crossref_primary_10_1016_j_cag_2025_104178 crossref_primary_10_1109_LRA_2022_3180038 crossref_primary_10_1109_TII_2023_3245682 crossref_primary_10_1109_TPAMI_2021_3092384 crossref_primary_10_1016_j_optlaseng_2025_109036 crossref_primary_10_1016_j_xnsj_2025_100770 crossref_primary_10_1007_s43670_025_00097_1 crossref_primary_10_1109_TASE_2023_3313773 crossref_primary_10_1016_j_atech_2023_100388 crossref_primary_10_1109_TIE_2024_3395792 crossref_primary_10_1109_ACCESS_2021_3135863 crossref_primary_10_1115_1_4069312 crossref_primary_10_1016_j_cag_2024_103974 crossref_primary_10_1007_s00530_024_01657_6 crossref_primary_10_1007_s11548_024_03255_3 crossref_primary_10_1109_TMRB_2025_3573420 crossref_primary_10_1016_j_knosys_2022_108182 crossref_primary_10_1007_s00371_022_02400_w crossref_primary_10_1007_s11740_023_01231_5 crossref_primary_10_1007_s11263_023_01759_0 crossref_primary_10_1080_01431161_2021_1975843 crossref_primary_10_3390_s24134144
Cites_doi	10.1007/978-3-540-24672-5_44 10.1007/3-540-47979-1_28 10.1109/ICIP.2016.7533212 10.1016/S1077-3142(03)00009-2 10.1109/TPAMI.2010.46 10.1007/978-3-642-33783-3_18 10.1109/CVPR.2007.383165 10.1007/BF01427149 10.1145/882262.882311 10.1162/neco.1995.7.2.219 10.1109/CVPR.2006.122 10.1145/1276377.1276478 10.1109/TPAMI.2006.213 10.1177/0278364912458814 10.1109/IM.2001.924423 10.1109/TIP.2015.2467217 10.1016/j.patcog.2014.06.017 10.1145/3130800.3130813 10.1109/TIP.2016.2540810 10.1109/34.121791 10.1145/1057432.1057435 10.1111/j.1467-8659.2008.01282.x 10.1109/CVPR.2018.00316 10.1137/0912004 10.1145/1015706.1015759 10.1016/j.imavis.2003.09.004 10.1016/j.jvcir.2017.03.012 10.1145/3130800.3130883 10.1109/TNNLS.2018.2872528 10.1016/j.patrec.2014.10.005 10.1145/361002.361007 10.1145/344779.344859 10.1007/BF00126430 10.1007/978-3-319-46475-6_47 10.1109/ICCV.2011.6126510 10.1109/TPAMI.2015.2513405 10.1145/1073204.1073323 10.1016/j.sigpro.2014.07.004 10.1145/146370.146374 10.1109/CVPRW.2015.7301306 10.1109/TMI.2003.819276 10.1109/LGRS.2015.2504268 10.1109/CVPR.2014.491 10.1007/3-540-63046-5_3 10.1145/1073204.1073207 10.1109/WACV.2015.20 10.1109/MMBIA.2000.852377 10.1016/j.imavis.2004.05.007 10.1109/TPAMI.2014.2316828 10.1109/ICCV.2003.1238383 10.1109/TPAMI.2015.2448102 10.1145/1015706.1015736 10.1109/TPAMI.2010.223 10.1109/CVPR.2017.423 10.1109/CVPR.2008.4587538 10.1109/TPAMI.2010.94 10.1111/j.1467-8659.2009.01373.x 10.1109/WACV.2016.7477719 10.1137/1.9781611970128
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
DBID	97E ESBDL RIA RIE AAYXX CITATION NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8
DOI	10.1109/TPAMI.2020.2971687
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitle	CrossRef PubMed Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitleList	Technology Research Database MEDLINE - Academic PubMed
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	2160-9292 1939-3539
EndPage	2286
ExternalDocumentID	32031931 10_1109_TPAMI_2020_2971687 8985307
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: JSPS KAKENHI grantid: 17K12712
GroupedDBID	--- -DZ -~X .DC 0R~ 29I 4.4 53G 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD ESBDL F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 UHB ~02 AAYXX CITATION NPM RIC Z5M 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8
ID	FETCH-LOGICAL-c461t-3f082a24f305cd39c4abbde36b62b99e12572319061e91058baae21f0f18fbd53
IEDL.DBID	RIE
ISICitedReferencesCount	120
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000692540900008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0162-8828 1939-3539
IngestDate	Sat Sep 27 18:09:14 EDT 2025 Sun Nov 09 06:56:49 EST 2025 Wed Feb 19 02:31:49 EST 2025 Sat Nov 29 05:15:59 EST 2025 Tue Nov 18 21:24:08 EST 2025 Wed Aug 27 02:51:03 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	7
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c461t-3f082a24f305cd39c4abbde36b62b99e12572319061e91058baae21f0f18fbd53
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-8077-8589
OpenAccessLink	https://ieeexplore.ieee.org/document/8985307
PMID	32031931
PQID	2539352495
PQPubID	85458
PageCount	18
ParticipantIDs	crossref_primary_10_1109_TPAMI_2020_2971687 proquest_journals_2539352495 pubmed_primary_32031931 proquest_miscellaneous_2352646438 ieee_primary_8985307 crossref_citationtrail_10_1109_TPAMI_2020_2971687
PublicationCentury	2000
PublicationDate	2021-07-01
PublicationDateYYYYMMDD	2021-07-01
PublicationDate_xml	– month: 07 year: 2021 text: 2021-07-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: New York
PublicationTitle	IEEE transactions on pattern analysis and machine intelligence
PublicationTitleAbbrev	TPAMI
PublicationTitleAlternate	IEEE Trans Pattern Anal Mach Intell
PublicationYear	2021
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	myronenko (ref1) 2006 ref57 ref13 ref12 ref59 ref15 ref58 ref14 ref52 ref55 ref11 ref54 ref10 ref17 ref16 ref19 ref18 ref51 ref50 (ref64) 0 ref46 ref48 ref47 ref42 ref41 ref44 ref43 williams (ref56) 2001 ref49 ref8 ref7 ref9 ref4 ref3 ref6 bishop (ref53) 2006 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref39 ref38 ref24 genton (ref62) 2001; 2 ref23 ref26 kraevoy (ref45) 2005 ref25 ref20 ref63 ref66 ref22 ref65 ref21 hirose (ref5) 2017 ref28 ref27 ref29 ref60 ref61
References_xml	– ident: ref37 doi: 10.1007/978-3-540-24672-5_44 – start-page: 682 year: 2001 ident: ref56 article-title: Using the Nyström method to speed up kernel machines publication-title: Proc Int Conf Neural Inf Process – start-page: 1009 year: 2006 ident: ref1 article-title: Non-rigid point set registration: Coherent point drift publication-title: Proc Int Conf Neural Inf Process – ident: ref9 doi: 10.1007/3-540-47979-1_28 – ident: ref15 doi: 10.1109/ICIP.2016.7533212 – ident: ref20 doi: 10.1016/S1077-3142(03)00009-2 – ident: ref2 doi: 10.1109/TPAMI.2010.46 – ident: ref38 doi: 10.1007/978-3-642-33783-3_18 – ident: ref30 doi: 10.1109/CVPR.2007.383165 – ident: ref33 doi: 10.1007/BF01427149 – ident: ref28 doi: 10.1145/882262.882311 – ident: ref54 doi: 10.1162/neco.1995.7.2.219 – ident: ref12 doi: 10.1109/CVPR.2006.122 – ident: ref31 doi: 10.1145/1276377.1276478 – ident: ref66 doi: 10.1109/TPAMI.2006.213 – ident: ref65 doi: 10.1177/0278364912458814 – ident: ref8 doi: 10.1109/IM.2001.924423 – ident: ref25 doi: 10.1109/TIP.2015.2467217 – ident: ref23 doi: 10.1016/j.patcog.2014.06.017 – ident: ref43 doi: 10.1145/3130800.3130813 – volume: 2 start-page: 299 year: 2001 ident: ref62 article-title: Classes of kernels for machine learning: A statistics perspective publication-title: J Mach Learn Res – ident: ref13 doi: 10.1109/TIP.2016.2540810 – ident: ref7 doi: 10.1109/34.121791 – ident: ref11 doi: 10.1145/1057432.1057435 – ident: ref32 doi: 10.1111/j.1467-8659.2008.01282.x – ident: ref17 doi: 10.1109/CVPR.2018.00316 – start-page: 1 year: 2017 ident: ref5 article-title: Dependent landmark drift: Robust point set registration with a Gaussian mixture model and a statistical shape model publication-title: arXiv 1711 06588v3 – ident: ref58 doi: 10.1137/0912004 – ident: ref63 doi: 10.1145/1015706.1015759 – ident: ref10 doi: 10.1016/j.imavis.2003.09.004 – ident: ref4 doi: 10.1016/j.jvcir.2017.03.012 – ident: ref44 doi: 10.1145/3130800.3130883 – ident: ref27 doi: 10.1109/TNNLS.2018.2872528 – ident: ref59 doi: 10.1016/j.patrec.2014.10.005 – ident: ref57 doi: 10.1145/361002.361007 – ident: ref40 doi: 10.1145/344779.344859 – ident: ref3 doi: 10.1007/BF00126430 – ident: ref39 doi: 10.1007/978-3-319-46475-6_47 – ident: ref42 doi: 10.1109/ICCV.2011.6126510 – ident: ref14 doi: 10.1109/TPAMI.2015.2513405 – ident: ref41 doi: 10.1145/1073204.1073323 – ident: ref22 doi: 10.1016/j.sigpro.2014.07.004 – ident: ref6 doi: 10.1145/146370.146374 – ident: ref51 doi: 10.1109/CVPRW.2015.7301306 – start-page: 13 year: 2005 ident: ref45 article-title: Template-based mesh completion publication-title: Proc Eurograph Symp Geometry Process – ident: ref34 doi: 10.1109/TMI.2003.819276 – year: 0 ident: ref64 – ident: ref61 doi: 10.1109/LGRS.2015.2504268 – ident: ref50 doi: 10.1109/CVPR.2014.491 – ident: ref35 doi: 10.1007/3-540-63046-5_3 – ident: ref46 doi: 10.1145/1073204.1073207 – ident: ref52 doi: 10.1109/WACV.2015.20 – ident: ref19 doi: 10.1109/MMBIA.2000.852377 – ident: ref36 doi: 10.1016/j.imavis.2004.05.007 – ident: ref18 doi: 10.1109/TPAMI.2014.2316828 – ident: ref60 doi: 10.1109/ICCV.2003.1238383 – ident: ref24 doi: 10.1109/TPAMI.2015.2448102 – ident: ref29 doi: 10.1145/1015706.1015736 – ident: ref21 doi: 10.1109/TPAMI.2010.223 – ident: ref16 doi: 10.1109/CVPR.2017.423 – ident: ref47 doi: 10.1109/CVPR.2008.4587538 – ident: ref49 doi: 10.1109/TPAMI.2010.94 – ident: ref48 doi: 10.1111/j.1467-8659.2009.01373.x – ident: ref26 doi: 10.1109/WACV.2016.7477719 – ident: ref55 doi: 10.1137/1.9781611970128 – year: 2006 ident: ref53 publication-title: Pattern Recognition and Machine Learning
SSID	ssj0014503
Score	2.6651487
Snippet	Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	2269
SubjectTerms	Algorithms Bayes methods Bayesian analysis Coherence coherent point drift Convergence Drift fast computation Inference algorithms Kernel Kernels Matrix converters motion coherence Non-rigid point set registration Parameters Rotation Shape Statistical inference Three-dimensional displays variational Bayesian inference
Title	A Bayesian Formulation of Coherent Point Drift
URI	https://ieeexplore.ieee.org/document/8985307 https://www.ncbi.nlm.nih.gov/pubmed/32031931 https://www.proquest.com/docview/2539352495 https://www.proquest.com/docview/2352646438
Volume	43
WOSCitedRecordID	wos000692540900008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2160-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014503 issn: 0162-8828 databaseCode: RIE dateStart: 19790101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Na9wwEB3SkEN7aJqkTd2mwYXeUm9sWbKl4_ZjaQ8Ne0hgb8aSRrAQ1mGzW-i_74zWNi00hVyMwLJsNDPMjEdvHsAHpwy3g3UZK0gmRW3JpGjka_Rl3eZeWRnJJuqrK71YmPkefByxMIgYD5_hhIexlu87t-VfZZfakHNh6PiTuq52WK2xYiBVZEGmCIYsnNKIASCTm8vr-fTHd0oFRT4R3DFJM_NeKRi_UxZ_-aNIsPJwrBl9zuzwcV_7Ap73sWU63SnDEezh6hgOB96GtDfjY3j2RxPCE5hM00_tL2QwZTqjCLbn80q7kDJ2g7s3pfNuSdcv62XYvISb2dfrz9-ynkUhc7IqNlkZyMu3QgaybOdL42RrrceyspWwxiBFODUFeYYcO5KYlLZti6IIeSh0sF6Vr2B_1a3wNaSUitSqkoXTGGgVp6X10issFHKntJBAMexl4_oW48x0cdvEVCM3TRRFw6JoelEkcDE-c7drsPHf2Se80ePMfo8TOBtE1vQ2eN8IxbBj5tZO4P14m6yHSyLtCrstzWF6AElRmU7gdCfqce1BQ978-51v4ang8y3x6O4Z7G_WW3wHB-7nZnm_PicVXejzqKK_AalF3Ew
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fa9RAEB5KK6gPVlutqVUj-Ka5JpvdS_bx_HG02B73cELfQnZ3Fg7kUq53gv-9M3uboKCCL2Ehm03YmWFmMvvNB_DWKs3tYG3GCpJJURkyKRq5Cl1ZtblTRgayiWo2q29u9HwP3g9YGEQMh89wxMNQy3ed3fKvsvNak3Nh6PgBM2dFtNZQM5Aq8CBTDEM2TolED5HJ9fliPrm-pGRQ5CPBPZNq5t4rBSN4yuI3jxQoVv4ebQavMz38v-99DI9idJlOdurwBPZwdQSHPXNDGg35CB7-0obwGEaT9EP7AxlOmU4pho2MXmnnU0ZvcP-mdN4t6fppvfSbp_B1-nnx8SKLPAqZleNik5We_HwrpCfbtq7UVrbGOCzHZiyM1kgxTkVhnibXjiQoVZu2RVH43Be1N06Vz2B_1a3wOaSUjFRqLAtbo6dVbC2Nk05hoZB7pfkEin4vGxubjDPXxbcmJBu5boIoGhZFE0WRwLvhmdtdi41_zj7mjR5mxj1O4KwXWROt8K4RioHHzK6dwJvhNtkPF0XaFXZbmsMEAZLisjqBk52oh7V7DTn98ztfw_2LxfVVc3U5-_ICHgg-7RIO8p7B_ma9xZdwz37fLO_Wr4Ki_gQdv96t
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Bayesian+Formulation+of+Coherent+Point+Drift&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=Hirose%2C+Osamu&rft.date=2021-07-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0162-8828&rft.eissn=1939-3539&rft.volume=43&rft.issue=7&rft.spage=2269&rft_id=info:doi/10.1109%2FTPAMI.2020.2971687&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-8828&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-8828&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-8828&client=summon