Rotation and Scale-Invariant Texture Classification Using Gabor Transform
نویسندگان
چکیده
Texture classification is one of the problems in the field of texture analysis. In this paper an efficient method of texture classification using Gabor transform is proposed, which considers the effect of rotation and scale variances of texture images. Due to its optimal localization properties in both spatial and frequency domain, the Gabor transform has been recognized as a very useful tool in texture analysis. The two texture features used are the mean and standard deviation of the Gabor filtered image. Rotation normalization is achieved by Circular Shift Technique of the feature elements. The texture similarity measurement between the training and the testing images is accomplished by minimum distance criterion.
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