creators_name: Spratling, Michael
type: journalp
datestamp: 2006-05-25
lastmod: 2011-03-11 08:56:26
metadata_visibility: show
title: Learning viewpoint invariant perceptual representations from cluttered images
ispublished: pub
subjects: neuro-mod
subjects: comp-sci-mach-vis
subjects: comp-sci-neural-nets
full_text_status: public
keywords: Computational models of vision; Neural Nets; invariance; object recognition
abstract: In order to perform object recognition, it is necessary to form perceptual representations that are sufficiently specific to distinguish between objects, but that are also sufficiently flexible to generalise across changes in location, rotation and scale. A standard method for learning perceptual representations that are invariant to viewpoint is to form temporal associations across image sequences showing object transformations. However, this method requires that individual stimuli are presented in isolation and is therefore unlikely to succeed in real-world applications where multiple objects can co-occur in the visual input. This article proposes a simple modification to the learning method, that can overcome this limitation, and results in more robust learning of invariant representations.

date: 2005
date_type: published
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
volume: 27
number: 5
pagerange: 753-761
refereed: TRUE
citation:   Spratling, Dr Michael  (2005) Learning viewpoint invariant perceptual representations from cluttered images.  [Journal (Paginated)]     
document_url: http://cogprints.org/4884/1/tpami05.pdf