End-Stop Exemplar Based Recognition

Søren I. Olsen

doi:10.1007/3-540-45103-X_7

End-Stop Exemplar Based Recognition

Søren I. Olsen

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

1 Citationer (Scopus)

Abstract

An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion.

Originalsprog	Engelsk
Titel	Image Analysis : 13th Scandinavian Conference, SCIA 2003 Halmstad, Sweden, June 29 – July 2, 2003 Proceedings
Publikationsdato	2003
Sider	257-266
DOI	https://doi.org/10.1007/3-540-45103-X_7
Status	Udgivet - 2003
Begivenhed	13th Scandinavian Conference in Image Analysis - SCIA - Halmstad, Sverige Varighed: 29 nov. 2010 → … Konferencens nummer: 13

Konference

Konference	13th Scandinavian Conference in Image Analysis - SCIA
Nummer	13
Land/Område	Sverige
By	Halmstad
Periode	29/11/2010 → …

Navn	Lecture notes in computer science
Vol/bind	2749/2003
ISSN	0302-9743

Adgang til dokumentet

10.1007/3-540-45103-X_7

http://www.springerlink.de/content/1c361a9edvhbt63t/fulltext.pdf

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Citationsformater

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title = "End-Stop Exemplar Based Recognition",

abstract = "An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion.",

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AB - An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion.

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