In this paper we investigate the benefits of using local wavelet analysis to the face recognition problem. We examine two possible approaches to perform local wavelet analysis. In the first approach, discrete wavelet ...
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ISBN:
(纸本)9781424407194
In this paper we investigate the benefits of using local wavelet analysis to the face recognition problem. We examine two possible approaches to perform local wavelet analysis. In the first approach, discrete wavelet transform is performed on the entire face image and then the transformed image is partitioned into non-overlapping rectangular blocks. In the second approach, as in the JPEG2000 standard, the input face image is first partitioned into non-overlapping rectangular blocks, and then on each block discrete wavelet transformation is performed. Proposed approaches are tested against the occlusion problem using the AR face database and significant improvements are observed in the face recognition performance.
The discrete wavelet transform was introduced as a linear operator. It works on signals that are modeled as functions from the integers into the real or complex numbers. Since many signals have finite function values,...
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ISBN:
(纸本)0819441929
The discrete wavelet transform was introduced as a linear operator. It works on signals that are modeled as functions from the integers into the real or complex numbers. Since many signals have finite function values, a linear discrete wavelet transform over a finite ring has been proposed recently. Another recent development is the research of nonlinear wavelet transforms triggered by the introduction of Sweldens' lifting scheme. This paper builds on these developments and defines an essentially nonlinear translation invariant discrete wavelet transform that works on signals that are functions from the integers into any finite set. As only discrete arithmetic is needed, such transforms can be calculated very time efficiently. The basic properties of these generalized discrete wavelet transforms are given along with explicit examples.
Sleep Apnoea Syndromes (SAS) is a sleep disorder which caused breathing pauses during sleep at night. There is various method of analyzing sleep EEG signals can be found in the literature. In this paper both linear;Di...
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ISBN:
(纸本)9781509055593
Sleep Apnoea Syndromes (SAS) is a sleep disorder which caused breathing pauses during sleep at night. There is various method of analyzing sleep EEG signals can be found in the literature. In this paper both linear;Discrete wavelet Transform (DWT) and non-linear;Approximate Entropy (ApEn) extraction methods were performed to differentiate features of each sleep stages between apnoea and healthy person. The efficiency of both extraction methods was compared by using ANOVA. In our study, we observed the non-linear feature of ApEn improves the ability to discriminate healthy and sleep apnoea at different sleep stages.
We introduce a general framework for computing the continuous wavelet transform (CWT). Included in this framework is an FFT implementation as well as fast algorithms which achieve O(1) complexity per wavelet coefficie...
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ISBN:
(纸本)0819422134
We introduce a general framework for computing the continuous wavelet transform (CWT). Included in this framework is an FFT implementation as well as fast algorithms which achieve O(1) complexity per wavelet coefficient. The general approach that we present allows a straight forward comparison among a large variety of implementations. In our framework, computation of the CWT is viewed as convolving the input signal with wavelet templates that are the oblique projection of the ideal wavelets into one subspace orthogonal to a second subspace. We present this idea and discuss and compare particular implementations.
We present examples of a new type of wavelet basis functions that are orthogonal across shifts, but not across scales. The analysis functions are low order splines while the synthesis functions are polynomial splines ...
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ISBN:
(纸本)0819422134
We present examples of a new type of wavelet basis functions that are orthogonal across shifts, but not across scales. The analysis functions are low order splines while the synthesis functions are polynomial splines of higher degree n2. The approximation power of these representations is essentially as good as that of the corresponding Battle- Lemarie orthogonal wavelet transform, with the difference that the present wavelet synthesis filters have a much faster decay. This last property, together with the fact that these transformation s are almost orthogonal, may be useful for image coding and data compression.
The main contribution of this work is a new paradigm for image compression. We describe a new multi-layered representation technique for images. An image is encoded as the superposition of one main approximation, and ...
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ISBN:
(纸本)0819429139
The main contribution of this work is a new paradigm for image compression. We describe a new multi-layered representation technique for images. An image is encoded as the superposition of one main approximation, and a sequence of residuals. The strength of the multi-layered method comes from the fact that we use different bases to encode the main approximation and the residuals. For instance, we can use: - a wavelet basis to encode a coarse main approximation of the image, - wavelet packet bases to encode textured patterns, - brushlet bases to encode localized oriented textured features, etc.
This paper presents implementation of the wavelet transform on parallel computers. The time of computation of wavelet transform on classic computers limits its applications in several areas of signalprocessing and da...
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ISBN:
(纸本)0819411973
This paper presents implementation of the wavelet transform on parallel computers. The time of computation of wavelet transform on classic computers limits its applications in several areas of signalprocessing and data compression. We examine some problems encountered when parallelizing such a code and we compare three different SIMD computers on this basis: a Connection-Machine 2/200, a SYMPATI-2 Line Processor, and a MasPar MP-1.
In this paper, we link concepts from nonuniform sampling, smoothness function spaces, interpolation, and wavelet denoising to derive a new multiscale interpolation algorithm for piecewise smooth signals. We formulate ...
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ISBN:
(纸本)0819450804
In this paper, we link concepts from nonuniform sampling, smoothness function spaces, interpolation, and wavelet denoising to derive a new multiscale interpolation algorithm for piecewise smooth signals. We formulate the optimization of finding the signal that balances agreement with the given samples against a wavelet-domain regularization. For signals in the Besov space B-p(alpha)(L-p), p greater than or equal to 1, the optimization corresponds to convex programming in the wavelet domain. The algorithm simultaneously achieves signal interpolation and wavelet denoising, which makes it particularly suitable for noisy sample data, unlike classical approaches such as bandlimited and spline interpolation.
Blocking artifacts are the most objectionable drawback of block-based image and video coders. We describe a novel technique for removing blocking artifacts via multiscale edge processing. The new technique exploits th...
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ISBN:
(纸本)0819422134
Blocking artifacts are the most objectionable drawback of block-based image and video coders. We describe a novel technique for removing blocking artifacts via multiscale edge processing. The new technique exploits the advantages of an invertible multiscale edge representation from which the block edges can be easily identified and removed. By virtue of the multiscale edge processing one is able to deblock images effectively without blurring perceptually important features or introducing new artifacts. We present the deblocking algorithm with experimental results and a discussion.
This paper presents a systematic high-speed VLSI implementation of the discrete wavelet transform (DWT) based on hardware-efficient parallel FIR filter structures. High-speed 2-D DWT with computation time as low as N-...
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This paper presents a systematic high-speed VLSI implementation of the discrete wavelet transform (DWT) based on hardware-efficient parallel FIR filter structures. High-speed 2-D DWT with computation time as low as N-2/12 can be easily achieved for an N x N image with controlled increase of hardware cost. Compared with recently published 2-D DWT architectures with computation time of N-2/3 and 2N(2)/3, the proposed designs can also save a large amount of multipliers and/or storage elements. It can also be used to implement those 2-D DWT traditionally suitable for lifting or flipping-based designs, such as (9,7) and (6,10) DWT. The throughput rate can be improved by a factor of 4 by the proposed approach, but the hardware cost increases by a factor of around 3. Furthermore, the proposed designs have very simple control signals, regular structures and 100% hardware utilization for continuous images.
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