Paper Info
Title
A Selective Attention Computational Model for Perceiving Textures
Abstract
This paper presents a biologically-inspired method of perceiving textures from various texture images. Our approach is motivated by a computational model of neuron cells found in the cerebral visual cortex. An unsupervised learning schemes of SOM(: Self-Organizing Map) is used for the block-based textures clustering, plus a selective attention computational model tuning to the response frequency properties of texture is used for perceiving any texture from the clustered texture. To evaluate the effectiveness of the proposed method, various texture images were built, and the quality of the perceived TROI(: Texture Region Of Interest) was measured according to the discrepancies. Our experimental results demonstrated a very successful performance.
Year
DOI
Venue
2008
10.1007/978-3-540-87734-9_49
ISNN (2)
Keywords
Field
DocType
various texture image,block-based texture,texture region,biologically-inspired method,selective attention,selective attention computational model,perceiving texture,computational model,perceiving textures,self-organizing map,cerebral visual cortex,self organization,unsupervised learning,computer model,region of interest
Computer vision,Visual cortex,Pattern recognition,Computer science,Selective attention,Unsupervised learning,Artificial intelligence,Region of interest,Cluster analysis,Machine learning
Conference
Volume
ISSN
Citations 
5264
0302-9743
0
PageRank 
References 
Authors
0.34
8
1
Name
Order
Citations
PageRank
Woo-Beom Lee193.96