Invariant Reference Points Methodology and Applications

Authors:
Krystian Ignasiak;Wladyslaw Skarbek;Miloud Ghuwar
Affiliations:
-;-;-
Venue:
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Year:
1999

Citing 6
Cited 0

Fractal image compression: theory and application

Fractal image compression: theory and application
Principal component neural networks: theory and applications

Principal component neural networks: theory and applications
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Self-Organizing Maps

Self-Organizing Maps
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Local Subspace Method for Pattern Recognition

CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns

Quantified Score

Hi-index	0.00

Visualization

Abstract

A methodology for pattern recognition based on design of invariant reference points is described. Within this framework many classical pattern classifiers can be described (e.g. the k-NN distance classifier) and few new classifiers are defined (e.g. LPCAS classifier). LPCAS is applied for handwritten digit recognition reaching performance of 99.4% of recognition rate on NIST database.