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The Wavelet Transform remained quite rapidly used technique today for analysing the signals. For image edge detection, wavelet transform provides facility to select the size of the image details that will be detected. Wavelets transform separates the lower frequencies and higher frequencies easily, which is prime important for edge detection. The wavelet scale sets the size of detected edges. For...
Medical Imaging is currently a hot area of biomedical engineers, researchers and medical doctors as it is extensively used in diagnosing of human health and by health care institutes. The imaging equipment is the device, which is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and transmission process...
Up to now, all the wavelet transforms are inner product representation of Hilbert space. In [1,2,3,4], based on the principle of mathematics and dialectical method, a novel method-VPWT (Vector Product Wavelet Transform) is proposed and studied, it is useful for wavelet theory and signal processing. In [5,6], vector wavelet functions, vector scaling functions and biased wavelet functions are put forward,...
This paper summarizes the recent development of illumination compensation for face recognition. Then a new method is proposed by extracting illumination invariant features in contourlet transform domain based on human visual perception mechanism. The experimental results on Yale B and CMU PIE face databases demonstrate that it is effective and competitive.
This paper firstly shows the entire picture of a promising research area, face recognition. Then a new face recognition method is proposed, mainly based on the combination of wavelet transform and local hybrid projection entropy (LHPE). Some classical image processing methods are included to constitute a cascade framework. The experimental results on Yale face database and Yale B face database show...
Given uncertain status reports or notes come from multi-sensor, identity fusion further makes them integrate information and jointly determine the observed entities. This paper discusses an improved data fusion approach to multi-biometric feature, including face, fingerprint and iris image. The approach is called improved multiple biometric data fusion algorithm, based on the eigen-face and the Gabor...
Image registration is an important task in the field of computer vision and pattern recognition. And the applied values is also reflected in the study of remote sensing, medical imaging and the object indentifying of multi-sensor fusion. In this paper, a new subpixel registration methods which based on wavelet analysis was proposed by improving polynomial subdivision algorithm and pixel level registration...
Wavelets transform have been extended to image process and achieve a lot of good results in the field. However, it is of congenital defects. So many new methods are generated based on it. Wedgelet transform is a better method among them. Wedgelet transform is a kind of multi-resolution method which can represent anisotropy of image. We induce the theory of wedgelet transform, in this paper, and apply...
Aiming at the problems of images denoising, wavelet algorithm is introduced simply. The technology of images denoising based on wavelet are analyzed in detail. Image denoising is achieved by Matlab. Two thresholding in the process of image denoising are compared by the results of experiments. Meanwhile, the advantages of image denoising by wavelet is proved.
In this paper, we introduce a face recognition approach based on the contourlet transform and support vector machine, which takes technological advantages of both support vector machine and the contourlet transform for feature extraction. The contributions of this paper include the following aspects: (1) support vector machine is successfully applied to face recognition by using the contourlet transform...
In this paper a face recognition algorithm based on multiscale wavelet representations and model adaptation is proposed. The models used in this work are from linear associative memory method and fast compensated in simulated testing phase by adoptively learning from the given simulated testing data. The proposed adaptation algorithm is incremental. It has low time and space complexity. Through compensating...
Quantum threshold algorithm is proposed to reduce the noise of signal. Quantum superposition principle is used to construct noise model in wavelet domain. We consider that signal is quasi quantum system. Every wavelet coefficient belongs to a superposition state. We don 't know whether it belongs signal or noise until we measure it. Unlike hard threshold algorithm quantum threshold algorithm hasn't...
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