Nonparametric Curve Alignment

Nonparametric Curve Alignment

Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This rese...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Mattar, Marwan, Ross, Michael, Learned-Miller, Erik
Format: Artikel
Sprache:eng
Schlagworte:
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Statistics - Machine Learning
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Mattar, Marwan
Ross, Michael
Learned-Miller, Erik
description Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. We present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous alignment and clustering.
doi_str_mv 10.48550/arxiv.1902.00626
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1902_00626</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1902_00626</sourcerecordid><originalsourceid>FETCH-LOGICAL-a676-1d26945a3fd7c5e4f9eee094358d14377336f95203d3d999e81ff7924b6b2ce23</originalsourceid><addsrcrecordid>eNotzrsKwjAUgOEsDlJ9AAexL9Ca5OTSM0rxBqKLe4nNiRRslXhB3168TP_28zE2EjxXhdZ86uKzeeQCucw5N9L02Xh77i4uupZusanT8h4flM5OzbFrqbsNWC-405WG_yZsv5jvy1W22S3X5WyTOWNNJrw0qLSD4G2tSQUkIo4KdOGFAmsBTEAtOXjwiEiFCMGiVAdzkDVJSNjkt_36qktsWhdf1cdZfZ3wBsc0Nmo</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Nonparametric Curve Alignment</title><source>arXiv.org</source><creator>Mattar, Marwan ; Ross, Michael ; Learned-Miller, Erik</creator><creatorcontrib>Mattar, Marwan ; Ross, Michael ; Learned-Miller, Erik</creatorcontrib><description>Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. We present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous alignment and clustering.</description><identifier>DOI: 10.48550/arxiv.1902.00626</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Learning ; Statistics - Machine Learning</subject><creationdate>2019-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1902.00626$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1902.00626$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Mattar, Marwan</creatorcontrib><creatorcontrib>Ross, Michael</creatorcontrib><creatorcontrib>Learned-Miller, Erik</creatorcontrib><title>Nonparametric Curve Alignment</title><description>Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. We present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous alignment and clustering.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Learning</subject><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzrsKwjAUgOEsDlJ9AAexL9Ca5OTSM0rxBqKLe4nNiRRslXhB3168TP_28zE2EjxXhdZ86uKzeeQCucw5N9L02Xh77i4uupZusanT8h4flM5OzbFrqbsNWC-405WG_yZsv5jvy1W22S3X5WyTOWNNJrw0qLSD4G2tSQUkIo4KdOGFAmsBTEAtOXjwiEiFCMGiVAdzkDVJSNjkt_36qktsWhdf1cdZfZ3wBsc0Nmo</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Mattar, Marwan</creator><creator>Ross, Michael</creator><creator>Learned-Miller, Erik</creator><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20190201</creationdate><title>Nonparametric Curve Alignment</title><author>Mattar, Marwan ; Ross, Michael ; Learned-Miller, Erik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-1d26945a3fd7c5e4f9eee094358d14377336f95203d3d999e81ff7924b6b2ce23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Learning</topic><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Mattar, Marwan</creatorcontrib><creatorcontrib>Ross, Michael</creatorcontrib><creatorcontrib>Learned-Miller, Erik</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mattar, Marwan</au><au>Ross, Michael</au><au>Learned-Miller, Erik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonparametric Curve Alignment</atitle><date>2019-02-01</date><risdate>2019</risdate><abstract>Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. We present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous alignment and clustering.</abstract><doi>10.48550/arxiv.1902.00626</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.1902.00626
ispartof
issn
language eng
recordid cdi_arxiv_primary_1902_00626
source arXiv.org
subjects Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Statistics - Machine Learning
title Nonparametric Curve Alignment
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T03%3A18%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Nonparametric%20Curve%20Alignment&rft.au=Mattar,%20Marwan&rft.date=2019-02-01&rft_id=info:doi/10.48550/arxiv.1902.00626&rft_dat=%3Carxiv_GOX%3E1902_00626%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true