Please use this identifier to cite or link to this item: https://doi.org/10.1016/0262-8856(95)01065-3
Title: Invariant object recognition using a neural template classifier
Authors: Tang, H.W.
Srinivasan, V. 
Ong, S.H. 
Keywords: Invariance
Neural networks
Object recognition
Template classifier
Issue Date: Jul-1996
Source: Tang, H.W.,Srinivasan, V.,Ong, S.H. (1996-07). Invariant object recognition using a neural template classifier. Image and Vision Computing 14 (7) : 473-483. ScholarBank@NUS Repository. https://doi.org/10.1016/0262-8856(95)01065-3
Abstract: This paper describes an efficient two-stage neural network for invariant object recognition. It consists of a feature extractor trained by an ART-like fast saturation learning scheme and a delta-rule trained classifier. Objects, represented as edge strength maps derived from raw input images, are scaled to a normalized size and rotated in discrete steps to generate a sequence of localized input feature vectors. The network outputs identify the object and permit the calculation of a confidence level. Experiments show that the system works well even when there is noise and occlusion.
Source Title: Image and Vision Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/62351
ISSN: 02628856
DOI: 10.1016/0262-8856(95)01065-3
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

5
checked on Dec 5, 2017

WEB OF SCIENCETM
Citations

3
checked on Nov 4, 2017

Page view(s)

25
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.