000			02445 a2200193 4500
003			Nust
005			20260206184552.0
040			_cNust
082			_a621.382,CHA
100			_aChaudhry, Mohammad Ali _9133106
245			_aDesign of Appropriate Wavelet Bases for Texture Discrimination / _cMohammad Ali Chaudhry
260			_bMCS (NUST), _aRawalpindi, _c2007
300			_axii, 83, p.;
505			_aProblem of texture analysis and discrimination using wavelet transform has been under consideration for the last three decades by several researchers from different fields. Unlike Fourier transform, several bases are available in wavelet transform for signal decomposition, but none of these has been designed by considering the actual texture images to be discriminated. Therefore, a system is required for the design of wavelet bases which caters the actual texture image to be discriminated. There are several factors involved in wavelet design which can greatly influence the desired results such as regularity, length of the wavelet, orthogonal or biorthogonal etc. In this research work, we have analyzed different regions of Pakistan on the basis of their textural properties. We propose a design of wavelet bases using genetic optimization, which will provide excellent discrimination between the multiple texture image s. Our objective function is based on maximization of distinguishability measure involving the computation of finer details subject to some wavelet constraints. In contrast to well known orthogonal wavelet families, we have used extra degree of freedom in design of the wavelet function for best possible texture discrimination. In genetic optimization process, design parameters of wavelet are optimized according to the characteristics of texture images under defined set of constraints. Classification results of optimized orthogonal and biorthogonal wavelet were compared with the existing wavelet families, which show that the results obtained are superior in terms of texture discrimination. The proposed system is capable of designing optimized wavelet by changing the input texture images for different applications such as medical images, satellite images, document analysis and industrial application etc.
650			_aPhD Electrical Engineering Thesis _9133107
651			_aPhD EE Thesis _9133108
700			_aSupervised by Dr. Muhammad Noman Jafri _9133109
942			_cTHE _2ddc
999			_c181776 _d181776