Loading…

Pattern recognition with generalized centroids and subcentroids

We propose a class of generalized moment functions (GMFs) that can be used to determine a set of geometric points, namely, generalized centroids (G centroids), within an object. Based on a linear GMF, a mass centroid and its subcentroids can be defined and extracted, which provide information on the...

Full description

Saved in:
Bibliographic Details
Published in:Applied optics (2004) 2005-03, Vol.44 (8), p.1372-1380
Main Authors: Chang, Shoude, Grover, Chander P
Format: Article
Language:English
Subjects:
Citations: 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-c289t-f57f07e607456b0ab891aad045f66012b75c0c295bf7f1b42bdecc3bbf1c78bb3
cites
container_end_page 1380
container_issue 8
container_start_page 1372
container_title Applied optics (2004)
container_volume 44
creator Chang, Shoude
Grover, Chander P
description We propose a class of generalized moment functions (GMFs) that can be used to determine a set of geometric points, namely, generalized centroids (G centroids), within an object. Based on a linear GMF, a mass centroid and its subcentroids can be defined and extracted, which provide information on the location and orientation of an object. Similar to traditional moment functions, GMFs can also be used to describe the global shape of an object, including symmetry and fullness. However, GMFs, along with G centroids and subcentroids, can further serve to construct a feature vector of an object, which is critical for image registration and invariant pattern recognition. One can extract more distinguishing features from the same object by changing the combination of GMFs. We present results that show simulations of pattern recognition from uniform backgrounds.
doi_str_mv 10.1364/AO.44.001372
format article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_miscellaneous_67557223</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>67557223</sourcerecordid><originalsourceid>FETCH-LOGICAL-c289t-f57f07e607456b0ab891aad045f66012b75c0c295bf7f1b42bdecc3bbf1c78bb3</originalsourceid><addsrcrecordid>eNo9j81LwzAchnNQ3JzePEtP3lrznfYkYzgVBvOg4K0k6S8z0qYzSRH96xWcnh54eXjhQeiC4Iowya-X24rzCmPCFD1CcyJEUxJav8zQaUpvGDPBG3WCZkSoRlLG5-jmUecMMRQR7LgLPvsxFB8-vxY7CBB177-gKyyEHEffpUKHrkiT-R_O0LHTfYLzAxfoeX37tLovN9u7h9VyU1paN7l0QjmsQGLFhTRYm7ohWneYCyclJtQoYbGljTBOOWI4NR1Yy4xxxKraGLZAV7-_-zi-T5ByO_hkoe91gHFKrVRCKErZj3h5ECczQNfuox90_Gz_ktk3AyVXFQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>67557223</pqid></control><display><type>article</type><title>Pattern recognition with generalized centroids and subcentroids</title><source>Optica Publishing Group Journals</source><creator>Chang, Shoude ; Grover, Chander P</creator><creatorcontrib>Chang, Shoude ; Grover, Chander P</creatorcontrib><description>We propose a class of generalized moment functions (GMFs) that can be used to determine a set of geometric points, namely, generalized centroids (G centroids), within an object. Based on a linear GMF, a mass centroid and its subcentroids can be defined and extracted, which provide information on the location and orientation of an object. Similar to traditional moment functions, GMFs can also be used to describe the global shape of an object, including symmetry and fullness. However, GMFs, along with G centroids and subcentroids, can further serve to construct a feature vector of an object, which is critical for image registration and invariant pattern recognition. One can extract more distinguishing features from the same object by changing the combination of GMFs. We present results that show simulations of pattern recognition from uniform backgrounds.</description><identifier>ISSN: 1559-128X</identifier><identifier>DOI: 10.1364/AO.44.001372</identifier><identifier>PMID: 15796234</identifier><language>eng</language><publisher>United States</publisher><subject>Algorithms ; Artificial Intelligence ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Imaging, Three-Dimensional - methods ; Information Storage and Retrieval - methods ; Numerical Analysis, Computer-Assisted ; Pattern Recognition, Automated - methods</subject><ispartof>Applied optics (2004), 2005-03, Vol.44 (8), p.1372-1380</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c289t-f57f07e607456b0ab891aad045f66012b75c0c295bf7f1b42bdecc3bbf1c78bb3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15796234$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chang, Shoude</creatorcontrib><creatorcontrib>Grover, Chander P</creatorcontrib><title>Pattern recognition with generalized centroids and subcentroids</title><title>Applied optics (2004)</title><addtitle>Appl Opt</addtitle><description>We propose a class of generalized moment functions (GMFs) that can be used to determine a set of geometric points, namely, generalized centroids (G centroids), within an object. Based on a linear GMF, a mass centroid and its subcentroids can be defined and extracted, which provide information on the location and orientation of an object. Similar to traditional moment functions, GMFs can also be used to describe the global shape of an object, including symmetry and fullness. However, GMFs, along with G centroids and subcentroids, can further serve to construct a feature vector of an object, which is critical for image registration and invariant pattern recognition. One can extract more distinguishing features from the same object by changing the combination of GMFs. We present results that show simulations of pattern recognition from uniform backgrounds.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Information Storage and Retrieval - methods</subject><subject>Numerical Analysis, Computer-Assisted</subject><subject>Pattern Recognition, Automated - methods</subject><issn>1559-128X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><recordid>eNo9j81LwzAchnNQ3JzePEtP3lrznfYkYzgVBvOg4K0k6S8z0qYzSRH96xWcnh54eXjhQeiC4Iowya-X24rzCmPCFD1CcyJEUxJav8zQaUpvGDPBG3WCZkSoRlLG5-jmUecMMRQR7LgLPvsxFB8-vxY7CBB177-gKyyEHEffpUKHrkiT-R_O0LHTfYLzAxfoeX37tLovN9u7h9VyU1paN7l0QjmsQGLFhTRYm7ohWneYCyclJtQoYbGljTBOOWI4NR1Yy4xxxKraGLZAV7-_-zi-T5ByO_hkoe91gHFKrVRCKErZj3h5ECczQNfuox90_Gz_ktk3AyVXFQ</recordid><startdate>20050310</startdate><enddate>20050310</enddate><creator>Chang, Shoude</creator><creator>Grover, Chander P</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope></search><sort><creationdate>20050310</creationdate><title>Pattern recognition with generalized centroids and subcentroids</title><author>Chang, Shoude ; Grover, Chander P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c289t-f57f07e607456b0ab891aad045f66012b75c0c295bf7f1b42bdecc3bbf1c78bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Information Storage and Retrieval - methods</topic><topic>Numerical Analysis, Computer-Assisted</topic><topic>Pattern Recognition, Automated - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Shoude</creatorcontrib><creatorcontrib>Grover, Chander P</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><jtitle>Applied optics (2004)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Shoude</au><au>Grover, Chander P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pattern recognition with generalized centroids and subcentroids</atitle><jtitle>Applied optics (2004)</jtitle><addtitle>Appl Opt</addtitle><date>2005-03-10</date><risdate>2005</risdate><volume>44</volume><issue>8</issue><spage>1372</spage><epage>1380</epage><pages>1372-1380</pages><issn>1559-128X</issn><abstract>We propose a class of generalized moment functions (GMFs) that can be used to determine a set of geometric points, namely, generalized centroids (G centroids), within an object. Based on a linear GMF, a mass centroid and its subcentroids can be defined and extracted, which provide information on the location and orientation of an object. Similar to traditional moment functions, GMFs can also be used to describe the global shape of an object, including symmetry and fullness. However, GMFs, along with G centroids and subcentroids, can further serve to construct a feature vector of an object, which is critical for image registration and invariant pattern recognition. One can extract more distinguishing features from the same object by changing the combination of GMFs. We present results that show simulations of pattern recognition from uniform backgrounds.</abstract><cop>United States</cop><pmid>15796234</pmid><doi>10.1364/AO.44.001372</doi><tpages>9</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1559-128X
ispartof Applied optics (2004), 2005-03, Vol.44 (8), p.1372-1380
issn 1559-128X
language eng
recordid cdi_proquest_miscellaneous_67557223
source Optica Publishing Group Journals
subjects Algorithms
Artificial Intelligence
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated - methods
title Pattern recognition with generalized centroids and subcentroids
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T21%3A49%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Pattern%20recognition%20with%20generalized%20centroids%20and%20subcentroids&rft.jtitle=Applied%20optics%20(2004)&rft.au=Chang,%20Shoude&rft.date=2005-03-10&rft.volume=44&rft.issue=8&rft.spage=1372&rft.epage=1380&rft.pages=1372-1380&rft.issn=1559-128X&rft_id=info:doi/10.1364/AO.44.001372&rft_dat=%3Cproquest_pubme%3E67557223%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c289t-f57f07e607456b0ab891aad045f66012b75c0c295bf7f1b42bdecc3bbf1c78bb3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=67557223&rft_id=info:pmid/15796234&rfr_iscdi=true