DocumentCode :
2655608
Title :
Texture segmentation using Gabor filters
Author :
Mital, Dinesh P.
Author_Institution :
Dept. of Health Inf., Univ. of Med. & Dentistry of New Jersey, Newark, NJ, USA
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
109
Abstract :
An unsupervised texture segmentation technique using multi-channel filtering has been proposed. The main advantage of this approach is that it can use simple statistics of gray values in the filtered images as texture features. This simplicity is due to the direct result of decomposition of the original image into several filtered images with limited spectral information. The main issues involved in this approach are: 1) functional characterization of the channels and number of channels, 2) extraction of appropriate texture features from the filtered images, 3) the relationship between the channels, and 4) integration of texture features from different channels to produce a reliable segmentation
Keywords :
feature extraction; filtering theory; image segmentation; image texture; unsupervised learning; Gabor filters; filtered images; functional characterization; gray values; image decomposition; limited spectral information; multi-channel filtering; reliable segmentation; simple statistics; texture feature extraction; texture features; unsupervised texture segmentation technique; Biomedical imaging; Biomedical informatics; Clustering algorithms; Filtering; Fourier transforms; Frequency; Gabor filters; Image segmentation; Intelligent systems; Pixel;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-6400-7
Type :
conf
DOI :
10.1109/KES.2000.885770
Filename :
885770
Link To Document :
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