作者:
Torres, MEGamero, LFlandrin, PAbry, PFacultad de Ingeniería
Universidad Nacional de Entre Ríos 3100 Paraná (E.R.) C.C. 57 Sue. 3 Argentina CNRS
URA 1325 Laboratoire de Physique-Ecole Normale Supérieure de Lyon 69364 Lyon cedex 07 46 Allée d'Ltalie France
In this paper, we consider the problem of detecting changes ill dynamical systems from the analysis of the signals they produce. A notion of continuous multiresolution entropy is introduced, which combine advantages s...
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ISBN:
(纸本)0819425915
In this paper, we consider the problem of detecting changes ill dynamical systems from the analysis of the signals they produce. A notion of continuous multiresolution entropy is introduced, which combine advantages stemming from both classical entropy and wavelet analysis. The relevance of the approach, together with its robustness in tile presence of moderate noise, is supported by numerical investigations.
Iris recognition system for identity authentication and verification is one of the most precise and accepted biometrics in the world. Portable iris system mostly used in law enforcement applications, has been increasi...
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ISBN:
(纸本)9781479959914
Iris recognition system for identity authentication and verification is one of the most precise and accepted biometrics in the world. Portable iris system mostly used in law enforcement applications, has been increasing more rapidly. The portable device, however, requires a narrow-bandwidth communication channel to transmit iris code or iris image. Though a full resolution of iris image is preferred for accurate recognition of individual, to minimize time in a narrow-bandwidth channel for emergency identification, image compression should be used to minimize the size of image. This paper has investigated the effects of compression particularly for iris image based on wavelet transformed image, using Spatial-orientation tree wavelet (STW), Embedded Zero tree wavelet (EZW) and Set Partitioning in hierarchical trees (SPIHT), to identify the most suitable image compression. In this paper, Haar wavelet transform is utilized for image compression and image decomposition, by varying the decomposition level. The results have been examined in terms of Peak signal to noise ratio (PSNR), Mean square Error (MSE), Bit per Pixel Ratio (BPP) and Compression ratio (CR). It has been evidently found that wavelet transform is more effective in the image compression, as recognition performance is minimally affected and the use of Haar transform is ideally suited. CASIA, MMU iris database have been used for this purpose.
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, the design of a video conference system is outlined. The proposed scheme allows the various attendees to access the multipoint video distribution center via channels of different nature and capacity wit...
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In this paper, the design of a video conference system is outlined. The proposed scheme allows the various attendees to access the multipoint video distribution center via channels of different nature and capacity without resorting to parallel bank of coders or multiple decoding-coding conversions. This approach allows to obtain a scalability of the user profile which is not present in DCT based video coders, where the distribution of the encoded video to different attendees is performed by using a unique bit rate adjusted according to the capacity of the worst connection. In this contribution a video coder based on spatio-temporal multiresolution pyramid generated by a 3D separable wavelet transform (WT) is proposed. Experimental results show the capability of the proposed method.
Amplitude deviation (AD), frequency deviation (FD) and phase deviation (PD) are the important compositions of power quality disturbance (PQD). To analyze PQD deeply, this paper introduces a wavelet-based method for se...
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ISBN:
(纸本)0819450804
Amplitude deviation (AD), frequency deviation (FD) and phase deviation (PD) are the important compositions of power quality disturbance (PQD). To analyze PQD deeply, this paper introduces a wavelet-based method for separating slight AD, FD and PD from a combined PQD, then quantifying and identifying them. The method is based on the fact that a linear-phase complex wavelet is certainly with an even real part and an odd imaginary part, or inversely. The distinctive characteristics of the method are: complex biorthogonal wavelet with the shortest smoothing filter (Haar filter), shift-invariant wavelet transform (WT) at a few scales, simple relationships between the WT coefficients and the magnitudes of AD, FD and PD, simple binary feature victor and binary-decimal conversion identifying process. These make the method simple, correct and fast.
Hiding data into images can be referred to as a constrained optimization problem. Hence, we proposed a reversible genetic watermarking scheme based on the wavelet packet transform in this paper. Genetic algorithm is u...
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ISBN:
(纸本)9781479953905
Hiding data into images can be referred to as a constrained optimization problem. Hence, we proposed a reversible genetic watermarking scheme based on the wavelet packet transform in this paper. Genetic algorithm is used to select an appropriate basis from permissible bases of wavelet packet transform to increase the robustness of the embedded data, and the binary string to be hidden is embedded into middle and high frequency coefficients of the wavelet packet transform. The scheme to embed each binary value a modified version of "difference expansion". Experimental results show that the proposed method can resist some imageprocessing methods, such as sharping operation, while keeping cover image quality and the amount of hidden data acceptable in real world applications.
This paper discussed on the effectiveness of the Morlet wavelet to generate new edited signal. The 122.4 second SAESUS strain signal was edited based on the Morlet wavelet coefficient amplitude level. Segments with wa...
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ISBN:
(纸本)9781424455614
This paper discussed on the effectiveness of the Morlet wavelet to generate new edited signal. The 122.4 second SAESUS strain signal was edited based on the Morlet wavelet coefficient amplitude level. Segments with wavelet coefficient amplitude level lower than Cut Off Level (COL) were removed. Furthermore, extracted fatigue damaged segments were retained and then were joined in order to gain new edited signal. The signal statistical parameter and fatigue damaging values should be as accurate as possible for all signals. From the analysis, the 25,000 !" was selected to be the optimum COL value since the level did not change the signal behaviour. This value gave a 14 % reduction in length with only 6.1 % reduction in the fatigue damage. This indicated that the Morlet wavelet can be successfully applied to compress the original signal without changing the main history as well.
imagery is collected much faster and in significantly greater quantities today compared to a few years ago. Accurate registration of this imagery is vital for comparing the similarities and differences between multipl...
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ISBN:
(纸本)0819441864
imagery is collected much faster and in significantly greater quantities today compared to a few years ago. Accurate registration of this imagery is vital for comparing the similarities and differences between multiple images. image registration is a significant component in computer vision and other pattern recognition problems, medical applications such as Medical Resonance images (MRI) and Positron Emission Tomography (PET), remotely sensed data for target location and identification, and super-resolution algorithms. Since human analysis is tedious and error prone for large data sets, we require an automatic, efficient, robust, and accurate method to register images. wavelet transforms have proven useful for a variety of signal and imageprocessing tasks. In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency. The shift-invariant wavelet transform is applied in translation estimation and a new rotation-invariant polar wavelet transform is effectively utilized in rotation estimation. We demonstrate the robustness of these redundant wavelet transforms for the registration of two images (i.e., translating or rotating an input image to a reference image), but extensions to larger data sets are feasible. We compare the registration accuracy of our redundant wavelet transforms to the "critically sampled" discrete wavelet transform using the Daubechies (7,9) wavelet to illustrate the power of our algorithm in the presence of significant additive white Gaussian noise and strongly translated or rotated images. The views expressed in this article are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U. S. Government.
Most of the information in a typical image is concentrated in a few regions. These are dominated by structures characterized by discontinuities in intensity. Inherent to the identification of significant structures is...
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ISBN:
(纸本)0819425915
Most of the information in a typical image is concentrated in a few regions. These are dominated by structures characterized by discontinuities in intensity. Inherent to the identification of significant structures is determining the location of the structures and assigning saliency values to them. In this paper, we present a wavelet based algorithm that identifies significant structures in an image. We first decompose the image using the wavelet transform. Then, for each pixel, a combining process computes a function that collects all the information available at the sub-band scales. The rationale of the method relies on the assumption that images and volumes possess scale-coherent structures. A structure is scale-coherent if it persists across the scales, or contains all frequencies. The detection of these features forms the core of our approach. We construct a combining function of the image by multiplying the sub-bands obtained from the wavelet transform. The result of this operation is a mask that delineates regions comprising of significant structures. This mask can then be used to provide saliency values at each pixel. Additional processing and image enhancement algorithms can now concentrate their efforts in regions where the mask has high values. We show how our method can be employed to remove noise, enhance, and smooth features in images arising from medical imaging modalities including MRI and mammography.
In the last few years, it has become apparent that traditional wavelet-based imageprocessing algorithms and models have significant shortcomings in their treatment of edge contours. The standard modeling paradigm exp...
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ISBN:
(纸本)0819450804
In the last few years, it has become apparent that traditional wavelet-based imageprocessing algorithms and models have significant shortcomings in their treatment of edge contours. The standard modeling paradigm exploits the fact that wavelet coefficients representing smooth regions in images tend to have small magnitude, and that the multiscale nature of the wavelet transform implies that these small coefficients will persist across scale (the canonical example is the venerable zero-tree coder). The edge contours in the image, however, cause more and more large magnitude wavelet coefficients as we move down through scale to finer resolutions. But if the contours are smooth, they become simple as we zoom in on them, and are well approximated by straight lines at fine scales. Standard wavelet models exploit the grayscale regularity of the smooth regions of the image, but not the geometric regularity of the contours. In this paper, we build a model that accounts for this geometric regularity by capturing the dependencies between complex wavelet coefficients along a contour. The Geometric Hidden Markov Tree (GHMT) assigns each wavelet coefficient (or spatial cluster of wavelet coefficients) a hidden state corresponding to a linear approximation of the local contour structure. The shift and rotational-invariance properties of the complex wavelet transform allow the GHMT to model the behavior of each coefficient given the presence of a linear edge at a specified orientation - the behavior of the wavelet coefficient given the state. By connecting the states together in a quadtree, the GHMT ties together wavelet coefficients along a contour, and also models how the contour itself behaves across scale. We demonstrate the effectiveness of the model by applying it to feature extraction.
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