Loading…

Multicriteria Robust Fitting of Elliptical Primitives

Geometric fitting is present in different fields of science, engineering and astronomy. In particular, ellipse shapes are some of the most commonly employed geometric features in digital image analysis and visual pattern recognition. Most geometric and algebraic methods are sensitive to noise and ou...

Full description

Saved in:
Bibliographic Details
Published in:Journal of mathematical imaging and vision 2014, Vol.49 (2), p.492-509
Main Authors: Muñoz-Pérez, J., de Cózar-Macías, O. D., Blázquez-Parra, E. B., Ladrón de Guevara-López, I.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c318t-68d4267ba8c89695032615e32a8720c74eda134f7c3fe520383a8463ba1423d3
cites cdi_FETCH-LOGICAL-c318t-68d4267ba8c89695032615e32a8720c74eda134f7c3fe520383a8463ba1423d3
container_end_page 509
container_issue 2
container_start_page 492
container_title Journal of mathematical imaging and vision
container_volume 49
creator Muñoz-Pérez, J.
de Cózar-Macías, O. D.
Blázquez-Parra, E. B.
Ladrón de Guevara-López, I.
description Geometric fitting is present in different fields of science, engineering and astronomy. In particular, ellipse shapes are some of the most commonly employed geometric features in digital image analysis and visual pattern recognition. Most geometric and algebraic methods are sensitive to noise and outlier points and so the results are not usually acceptable. In this paper, a robust geometric multicriteria method based on the mean absolute geometric error and the eccentricity to fit an ellipse to set of points is proposed. It is well known that the least mean absolute error criterion leads to robust estimations. The experimental results on different real and synthetic data have shown that the proposed algorithm is robust to outliers. Moreover, it allows us to identify outliers and remove them.
doi_str_mv 10.1007/s10851-013-0480-1
format article
fullrecord <record><control><sourceid>pascalfrancis_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1007_s10851_013_0480_1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>28422876</sourcerecordid><originalsourceid>FETCH-LOGICAL-c318t-68d4267ba8c89695032615e32a8720c74eda134f7c3fe520383a8463ba1423d3</originalsourceid><addsrcrecordid>eNp9j8FKAzEURYMoWKsf4G42LqPvJZkks5TSqlBRpPuQSTMlZZwpSSr496aMuHT1Fu-eyz2E3CLcI4B6SAi6RgrIKQgNFM_IDGvFqZKan5MZNEzQpgF1Sa5S2gOAZqhmpH499jm4GLKPwVYfY3tMuVqFnMOwq8auWvZ9OJSE7av3GD5DDl8-XZOLzvbJ3_zeOdmslpvFM12_Pb0sHtfUcdSZSr0VTKrWaqcb2dTAmcTac2a1YuCU8FuLXHTK8c7XDLjmVgvJW4uC8S2fE5xqXRxTir4zhzLBxm-DYE7aZtI2RductA0W5m5iDjaV0V20gwvpD2RaMKaVLDk25VJ5DTsfzX48xqHY_FP-Az42ZjQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Multicriteria Robust Fitting of Elliptical Primitives</title><source>Springer Nature</source><creator>Muñoz-Pérez, J. ; de Cózar-Macías, O. D. ; Blázquez-Parra, E. B. ; Ladrón de Guevara-López, I.</creator><creatorcontrib>Muñoz-Pérez, J. ; de Cózar-Macías, O. D. ; Blázquez-Parra, E. B. ; Ladrón de Guevara-López, I.</creatorcontrib><description>Geometric fitting is present in different fields of science, engineering and astronomy. In particular, ellipse shapes are some of the most commonly employed geometric features in digital image analysis and visual pattern recognition. Most geometric and algebraic methods are sensitive to noise and outlier points and so the results are not usually acceptable. In this paper, a robust geometric multicriteria method based on the mean absolute geometric error and the eccentricity to fit an ellipse to set of points is proposed. It is well known that the least mean absolute error criterion leads to robust estimations. The experimental results on different real and synthetic data have shown that the proposed algorithm is robust to outliers. Moreover, it allows us to identify outliers and remove them.</description><identifier>ISSN: 0924-9907</identifier><identifier>EISSN: 1573-7683</identifier><identifier>DOI: 10.1007/s10851-013-0480-1</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Applications of Mathematics ; Applied sciences ; Artificial intelligence ; Computer Science ; Computer science; control theory; systems ; Exact sciences and technology ; Image Processing and Computer Vision ; Mathematical Methods in Physics ; Pattern recognition. Digital image processing. Computational geometry ; Signal,Image and Speech Processing ; Theoretical computing</subject><ispartof>Journal of mathematical imaging and vision, 2014, Vol.49 (2), p.492-509</ispartof><rights>Springer Science+Business Media New York 2013</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c318t-68d4267ba8c89695032615e32a8720c74eda134f7c3fe520383a8463ba1423d3</citedby><cites>FETCH-LOGICAL-c318t-68d4267ba8c89695032615e32a8720c74eda134f7c3fe520383a8463ba1423d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=28422876$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Muñoz-Pérez, J.</creatorcontrib><creatorcontrib>de Cózar-Macías, O. D.</creatorcontrib><creatorcontrib>Blázquez-Parra, E. B.</creatorcontrib><creatorcontrib>Ladrón de Guevara-López, I.</creatorcontrib><title>Multicriteria Robust Fitting of Elliptical Primitives</title><title>Journal of mathematical imaging and vision</title><addtitle>J Math Imaging Vis</addtitle><description>Geometric fitting is present in different fields of science, engineering and astronomy. In particular, ellipse shapes are some of the most commonly employed geometric features in digital image analysis and visual pattern recognition. Most geometric and algebraic methods are sensitive to noise and outlier points and so the results are not usually acceptable. In this paper, a robust geometric multicriteria method based on the mean absolute geometric error and the eccentricity to fit an ellipse to set of points is proposed. It is well known that the least mean absolute error criterion leads to robust estimations. The experimental results on different real and synthetic data have shown that the proposed algorithm is robust to outliers. Moreover, it allows us to identify outliers and remove them.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applications of Mathematics</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer Science</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Image Processing and Computer Vision</subject><subject>Mathematical Methods in Physics</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Signal,Image and Speech Processing</subject><subject>Theoretical computing</subject><issn>0924-9907</issn><issn>1573-7683</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9j8FKAzEURYMoWKsf4G42LqPvJZkks5TSqlBRpPuQSTMlZZwpSSr496aMuHT1Fu-eyz2E3CLcI4B6SAi6RgrIKQgNFM_IDGvFqZKan5MZNEzQpgF1Sa5S2gOAZqhmpH499jm4GLKPwVYfY3tMuVqFnMOwq8auWvZ9OJSE7av3GD5DDl8-XZOLzvbJ3_zeOdmslpvFM12_Pb0sHtfUcdSZSr0VTKrWaqcb2dTAmcTac2a1YuCU8FuLXHTK8c7XDLjmVgvJW4uC8S2fE5xqXRxTir4zhzLBxm-DYE7aZtI2RductA0W5m5iDjaV0V20gwvpD2RaMKaVLDk25VJ5DTsfzX48xqHY_FP-Az42ZjQ</recordid><startdate>2014</startdate><enddate>2014</enddate><creator>Muñoz-Pérez, J.</creator><creator>de Cózar-Macías, O. D.</creator><creator>Blázquez-Parra, E. B.</creator><creator>Ladrón de Guevara-López, I.</creator><general>Springer US</general><general>Springer</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>2014</creationdate><title>Multicriteria Robust Fitting of Elliptical Primitives</title><author>Muñoz-Pérez, J. ; de Cózar-Macías, O. D. ; Blázquez-Parra, E. B. ; Ladrón de Guevara-López, I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c318t-68d4267ba8c89695032615e32a8720c74eda134f7c3fe520383a8463ba1423d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applications of Mathematics</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer Science</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Image Processing and Computer Vision</topic><topic>Mathematical Methods in Physics</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Signal,Image and Speech Processing</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Muñoz-Pérez, J.</creatorcontrib><creatorcontrib>de Cózar-Macías, O. D.</creatorcontrib><creatorcontrib>Blázquez-Parra, E. B.</creatorcontrib><creatorcontrib>Ladrón de Guevara-López, I.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><jtitle>Journal of mathematical imaging and vision</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Muñoz-Pérez, J.</au><au>de Cózar-Macías, O. D.</au><au>Blázquez-Parra, E. B.</au><au>Ladrón de Guevara-López, I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multicriteria Robust Fitting of Elliptical Primitives</atitle><jtitle>Journal of mathematical imaging and vision</jtitle><stitle>J Math Imaging Vis</stitle><date>2014</date><risdate>2014</risdate><volume>49</volume><issue>2</issue><spage>492</spage><epage>509</epage><pages>492-509</pages><issn>0924-9907</issn><eissn>1573-7683</eissn><abstract>Geometric fitting is present in different fields of science, engineering and astronomy. In particular, ellipse shapes are some of the most commonly employed geometric features in digital image analysis and visual pattern recognition. Most geometric and algebraic methods are sensitive to noise and outlier points and so the results are not usually acceptable. In this paper, a robust geometric multicriteria method based on the mean absolute geometric error and the eccentricity to fit an ellipse to set of points is proposed. It is well known that the least mean absolute error criterion leads to robust estimations. The experimental results on different real and synthetic data have shown that the proposed algorithm is robust to outliers. Moreover, it allows us to identify outliers and remove them.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s10851-013-0480-1</doi><tpages>18</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0924-9907
ispartof Journal of mathematical imaging and vision, 2014, Vol.49 (2), p.492-509
issn 0924-9907
1573-7683
language eng
recordid cdi_crossref_primary_10_1007_s10851_013_0480_1
source Springer Nature
subjects Algorithmics. Computability. Computer arithmetics
Applications of Mathematics
Applied sciences
Artificial intelligence
Computer Science
Computer science
control theory
systems
Exact sciences and technology
Image Processing and Computer Vision
Mathematical Methods in Physics
Pattern recognition. Digital image processing. Computational geometry
Signal,Image and Speech Processing
Theoretical computing
title Multicriteria Robust Fitting of Elliptical Primitives
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T17%3A51%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multicriteria%20Robust%20Fitting%20of%20Elliptical%20Primitives&rft.jtitle=Journal%20of%20mathematical%20imaging%20and%20vision&rft.au=Mu%C3%B1oz-P%C3%A9rez,%20J.&rft.date=2014&rft.volume=49&rft.issue=2&rft.spage=492&rft.epage=509&rft.pages=492-509&rft.issn=0924-9907&rft.eissn=1573-7683&rft_id=info:doi/10.1007/s10851-013-0480-1&rft_dat=%3Cpascalfrancis_cross%3E28422876%3C/pascalfrancis_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c318t-68d4267ba8c89695032615e32a8720c74eda134f7c3fe520383a8463ba1423d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true