A fast probabilistic model for hypothesis rejection in SIFT-based object recognition

Author	dc.contributor.author	Loncomilla, Patricio
Author	dc.contributor.author	Ruiz del Solar, Javier	es_CL
Admission date	dc.date.accessioned	2009-04-15T12:20:13Z
Available date	dc.date.available	2009-04-15T12:20:13Z
Publication date	dc.date.issued	2006
Cita de ítem	dc.identifier.citation	PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS Book Series: LECTURE NOTES IN COMPUTER SCIENCE Volume: 4225 Pages: 696-705 Published: 2006	en
Identifier	dc.identifier.issn	0302-9743
Identifier	dc.identifier.uri	https://repositorio.uchile.cl/handle/2250/124908
Abstract	dc.description.abstract	This paper proposes an improvement over the traditional SIFT-based object recognition methodology proposed by Lowe [3]. This improvement corresponds to a fast probabilistic model for hypothesis rejection (affine solution verification stage), which allows a large reduction in the number of false positives. The new probabilistic model is evaluated in an object recognition task using a database of 100 pairs of images.	en
Lenguage	dc.language.iso	en	en
Publisher	dc.publisher	SPRINGER-VERLAG BERLIN	en
Keywords	dc.subject	SCALE	en
Título	dc.title	A fast probabilistic model for hypothesis rejection in SIFT-based object recognition	en
Document type	dc.type	Artículo de revista