M. Emin Yüksel

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A new impulse noise detector based on neuro-fuzzy methods is presented. The proposed detector comprises two identical neuro-fuzzy subdetectors combined with a decision maker. The internal parameters of the subdetectors are adaptively adjusted by training. Training of the subdetectors is accomplished by using a simple computer generated artificial image. The(More)
A new operator for restoring digital images corrupted by impulse noise is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using(More)
The real time application of autoregressive (AR) spectral analysis to a 20-MHz pulsed Doppler blood flowmeter is presented. The system consists of a TMS 320C25 digital signal processor with a 80286 based PC/AT microcomputer and associated interfacing circuitry. The AR method was used for in vivo spectral analysis of the signals obtained from a 20-MHz pulsed(More)
A new operator for the restoration of digital images corrupted by impulse noise is presented. The proposed operator is a simple recursive switching median filter guided by a neuro-fuzzy network functioning as an impulse detector. The internal parameters of the neuro-fuzzy impulse detector are adaptively optimized by training. The training is easily(More)
In this paper, we present a novel application of type-2 fuzzy logic to the design of an image processing operator called an impulse detector. The type-2 fuzzy logic based impulse detector can be used to guide impulse noise removal filters to significantly improve their filtering performance and enhance their output images. The design of the proposed impulse(More)
In this paper, a new method for inspection of textile defects in fabrics is presented. The method is based upon the extraction of fabric features by Gabor wavelets. The Gabor wavelets transform provides an effective way to analyze images and extract features of textures. Principal component analysis using singular value decomposition is used to reduce the(More)