image mosaic combines two or more images. It has found many applications in computer vision, imageprocessing, and computer graphics. A common goal of the problem is to join two or more images such that there is an in...
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ISBN:
(纸本)0780367251
image mosaic combines two or more images. It has found many applications in computer vision, imageprocessing, and computer graphics. A common goal of the problem is to join two or more images such that there is an invisible boundary around the seam line and the mosaic image is as little distortion from the original images as possible. We propose a new image mosaic method by wavelet multiresolution analysis and variational calculus. We first project the images into wavelet spaces. The projected images at each wavelet space are then blended. In our approach, variational calculus techniques are applied to balance the quality between the smoothness around the seam line and the fidelity of the combined image relative to the original images in image blending. A mosaic image is finally obtained by summing the blended images at the wavelet spaces. Experimental results based on our method are demonstrated.
This paper presents an extensive study of the wavelet transform of self-similar signals and its properties. In particular, theorems are derived for the wavelet transform of deterministic self-similar signals and are u...
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ISBN:
(纸本)0819416274;9780819416278
This paper presents an extensive study of the wavelet transform of self-similar signals and its properties. In particular, theorems are derived for the wavelet transform of deterministic self-similar signals and are used to identify and characterize them. applications of interest, such as characterization and analysis of real chaotic signals in the presence of additive noise, are included.
Hidden Markov models have been used in a wide variety of wavelet-based statistical signalprocessingapplications. Typically, Gaussian mixture distributions are used to model the wavelet coefficients and the correlati...
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ISBN:
(纸本)0780370414
Hidden Markov models have been used in a wide variety of wavelet-based statistical signalprocessingapplications. Typically, Gaussian mixture distributions are used to model the wavelet coefficients and the correlation between the magnitudes of the wavelet coefficients within each scale and/or across the scales is captured by a Markov tree imposed on the (hidden) states of the mixture. This paper investigates correlations directly among the wavelet coefficient amplitudes (sign x magnitude), instead of magnitudes alone. Our theoretical analysis shows that the coefficients display significant correlations in sign as well as magnitude, especially near strong edges. We propose a new wavelet-based HMM structure based on mixtures of one-sided exponential densities that exploits both sign and magnitude correlations. We also investigate the application of this for denoising the signals corrupted by additive white Gaussian noise. Using some examples with standard test signals, we show that our new method can achieve better mean squared error, and the resulting denoised signals are generally much smoother.
Unlike the classical wavelet decomposition scheme it is possible to have different scaling and wavelet functions at every scale by using non-stationary multiresolution analyses. For the bidimensional case inhomogeneou...
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ISBN:
(纸本)0819416274
Unlike the classical wavelet decomposition scheme it is possible to have different scaling and wavelet functions at every scale by using non-stationary multiresolution analyses. For the bidimensional case inhomogeneous multiresolution analyses using different scaling and wavelet functions for the two variables are introduced. Beyond it, these two methods are combined. All this freedom is used for compact image coding. The idea is to build out of the functions in a library that special non-stationary and/or inhomogeneous multiresolution analysis, that is best suited for a given image in the context of compact coding (in the sense of optimizing certain cost-functions).
In this paper, we propose a new wideband bearing estimation method based on wavelet transform. By analyzing the relationship between the wavelet transform of the frequency invariant beam's output and the array'...
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ISBN:
(纸本)9781479902699;9781479902675
In this paper, we propose a new wideband bearing estimation method based on wavelet transform. By analyzing the relationship between the wavelet transform of the frequency invariant beam's output and the array's beampattern, we derived spatial power spectrum based on wavelet transform (SPS-WT). The method has good performance on noise suppression by utilizing the statistical uncorrelation character between signals and noise, and also has high resolution on bearing estimation. The performance of the proposed method is illustrated in simulation results.
The phase-coded image of a scene results from the projection of a grid on the objects of the scene. We show how the wavelet transform can be used as an oriented pattern detector for the segmentation of phase-coded ima...
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ISBN:
(纸本)0819416274;9780819416278
The phase-coded image of a scene results from the projection of a grid on the objects of the scene. We show how the wavelet transform can be used as an oriented pattern detector for the segmentation of phase-coded images. The detector coupled with a rotation of the digital camera enables detection of 3D planar surfaces. A digital image rotation technique using a scaling function from multiresolution analysis is also presented. Discussion of Fourier transform methods of segmentation of phase-coded images motivates the oriented pattern detection approach.
A discrete random wavelet transformation is defined as a random field of stochastic integrals of translates and dilates of a compactly supported wavelet function with respect to a fractional Brownian motion. It is sho...
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ISBN:
(纸本)0819416274;9780819416278
A discrete random wavelet transformation is defined as a random field of stochastic integrals of translates and dilates of a compactly supported wavelet function with respect to a fractional Brownian motion. It is shown that the transformation exhibits several properties analogous to the ones demonstrated previously for the `classical' wavelet transformation of a fractional Brownian motion. Moreover, some important advantages of the transformation defined here, over its `classical' counterpart, are demonstrated.
25 years after the seminal work of Jean Morlet, the wavelet transform, multiresolution analysis, and other space frequency or space scale approaches are considered standard tools by researchers in imageprocessing, an...
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ISBN:
(纸本)9780819464514
25 years after the seminal work of Jean Morlet, the wavelet transform, multiresolution analysis, and other space frequency or space scale approaches are considered standard tools by researchers in imageprocessing, and many applications have been proposed that point out the interest of these techniques. This paper proposes a review of the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. More than 180 recent papers are presented.
This paper develops new algorithms for adapted multiscale analysis and signal adaptive wavelet transforms. We construct our adaptive transforms with the lifting scheme, which decomposes the wavelet transform into pred...
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ISBN:
(纸本)0819432997
This paper develops new algorithms for adapted multiscale analysis and signal adaptive wavelet transforms. We construct our adaptive transforms with the lifting scheme, which decomposes the wavelet transform into prediction and update stages. We adapt the prediction stage to the signal structure and design the update stage to preserve the desirable properties of the wavelet transform. The resulting scale and spatially adaptive transforms are extended to the image estimation problem;our new image transforms show improved denoising performance over existing (non-adaptive) orthogonal transforms.
In this paper, a new approach to the feature extraction for incoherent radar ship target recognition is proposed, based on the discrete dynamic wavelet transform. Experiments are carried out for the method with practi...
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ISBN:
(纸本)0819416274;9780819416278
In this paper, a new approach to the feature extraction for incoherent radar ship target recognition is proposed, based on the discrete dynamic wavelet transform. Experiments are carried out for the method with practical video-echo data of four kinds. The corresponding results indicate that the extracted features are typical with high data compression.
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