Paper Info
Title
Indiscernibility Relation for Continuous Attributes: Application in Image Recognition
Abstract
The paper presents an application of rough sets in a problem defined for the continuous feature space used by hybrid, high speed, pattern recognition system. The feature extraction part of this system is built as a holographic ring-wedge detector based on binary grating. Such feature extractor can be optimized and we apply for this purpose automatic knowledge acquisition and processing. Features from optimized extractor are then classified with the use of probabilistic neural network classifier. The methodology, proposed by one of the authors in earlier works, has been further enhanced here by application of modified indiscernibility relation. Modified version of this relation makes possible natural application of discrete type rough knowledge representation to problems defined in continuous space. We present an application of modified indiscernibility relation in the domain of image recognition.
Year
DOI
Venue
2007
10.1007/978-3-540-73451-2_76
RSEISP
Keywords
Field
DocType
feature space,probabilistic neural network,feature extraction,image recognition,knowledge representation,pattern recognition,rough set
Data mining,Artificial intelligence,Classifier (linguistics),Binary number,Computer vision,Knowledge representation and reasoning,Feature vector,Pattern recognition,Feature extraction,Probabilistic neural network,Rough set,Mathematics,Knowledge acquisition
Conference
Volume
ISSN
Citations 
4585
0302-9743
6
PageRank 
References 
Authors
0.65
7
2
Name
Order
Citations
PageRank
Krzysztof A. Cyran1246.25
Urszula Stanczyk2193.75