Title
A Fast Gabor Filter Approach For Multi-Channel Texture Feature Discrimination
Abstract
Texture is a very important concept for many image understanding and pattern classification applications. The analysis of texture can be performed by the multi-channel filtering theory, a classical theory for texture perception based on the early stages of human visual system. This approach decomposes an image into a set of responses given by a bank of Gabor filters, that nearly covers in an uniformly manner the spatial-frequency domain. This approach relies on the image dimensions, and the number of kernels in a bank of Gabor filters varies according to the number of combinations between frequencies and orientations. In many practical applications, this large number of combinations makes quickly unfeasible the computation of the whole bank of filters. To ease this problem, in this paper we propose a multi-channel filtering where the Gabor bank for texture discrimination is computed in parallel in a graphics processing unit (GPU). Experimental results show an improvement of 8.78 times for feature extraction when compared against the corresponding CPU-based approach.
Year
DOI
Venue
2014
10.1007/978-3-319-12568-8_17
PROGRESS IN PATTERN RECOGNITION IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2014
Field
DocType
Volume
Computer vision,Pattern recognition,Human visual system model,Image texture,Gabor wavelet,Computer science,Filter (signal processing),Gabor filter,Feature extraction,Artificial intelligence,Graphics processing unit,Texture filtering
Conference
8827
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
8
4
Name
Order
Citations
PageRank
Antonio Carlos Sobieranski1114.23
Rodrigo Linhares282.27
Eros Comunello36615.04
Aldo von Wangenheim420949.44