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Articles 1 - 14 of 14
Full-Text Articles in Physical Sciences and Mathematics
Timing Of Substorm-Associated Auroral Oscillations, P Martin, Niescja E. Turner, J Wanliss
Timing Of Substorm-Associated Auroral Oscillations, P Martin, Niescja E. Turner, J Wanliss
Niescja E. Turner
Previous studies have shown that auroral luminosity oscillations are often associated with substorms. Here we examine photometer data for the magnetospheric substorm on April 1, 2000 (expansive phase onset at 0525 UT) to study the detailed timing of the auroral oscillations relative to onset. Accurate timing information for the periodicities in the photometer data were determined using the wavelet transform. We find that the oscillations occur primarily during the recovery phase. Copyright © The Society of Geomagnetism and Earth
Image Similarity Index Based On Moment Invariants Of Approximation Level Of Discrete Wavelet Transform, Prashan Premaratne, Malin Premaratne
Image Similarity Index Based On Moment Invariants Of Approximation Level Of Discrete Wavelet Transform, Prashan Premaratne, Malin Premaratne
Dr Prashan Premaratne
Subjective quality measures based on the human visual system for images do not agree well with well-known metrics such as mean squared error and peak signal-to-noise ratio. Recently, the structural similarity measure (SSIM) has received acclaim owing to its ability to produce results on a par with the human visual system. However, experimental results indicate that noise and blur seriously degrade the performance of the SSIM metric. Furthermore, despite the SSIM's popularity, it does not provide adequate insight into how it handles the 'structural similarity' of images. Proposed is a new structural similarity measure based on the approximation level of …
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Associate Professor Golshah Naghdy
A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ‘‘lowlevel’’ features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ‘‘cognitive system.’’ The novelty of the model developed in this paper lies in the use of a self-organizing input layer …
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
Associate Professor Golshah Naghdy
A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, Fuzzy Adaptive Resonance Theory (Fuzzy ART), acts as the high level decision making and recognition system. Some modifications to the Fuzzy ART make it capable of simulating the post-natal and evolutionary development of the human visual system. The proposed system has been evaluated using …
Wavelet Based Nonlocal-Means Super-Resolution For Video Sequences, H Zheng, A Bouzerdoum, S L. Phung
Wavelet Based Nonlocal-Means Super-Resolution For Video Sequences, H Zheng, A Bouzerdoum, S L. Phung
Professor Salim Bouzerdoum
Video sequence resolution enhancement became a popular research area during the last two decades. Although traditional super-resolution techniques have been successful in dealing with image sequences, many constraints such as global translation between frames, have to be imposed to obtain good performance. In this paper, we present a new wavelet-based nonlocal-means (WNLM) framework to bypass the motion estimation stage. It can handle complex motion changes between frames. Compared with the nonlocal-means (NLM) super-resolution framework, the proposed method provides better result in terms of PSNR and faster processing.
Coding Gain And Spatial Localisation Properties Of Discrete Wavelet Transform Filters For Image Coding, J Andrew, P Ogunbona, F Paoloni
Coding Gain And Spatial Localisation Properties Of Discrete Wavelet Transform Filters For Image Coding, J Andrew, P Ogunbona, F Paoloni
Professor Philip Ogunbona
The authors consider coding gain and spatial localisation properties of DWT filters for still image compression. Using a JPEG type quantisation and encoding method several images are compressed using a DWT implemented using various two-band subband filter sets. It is concluded that a relatively high coding gain (relative to a highly correlated source) is necessary, but not sufficient, for good image coding performance. Further, it is observed that low spatial width filters are desirable, particularly in regard to reduced ringing distortion. In terms of the tradeoff between coding gain and spatial localisation, and in terms of actual coding performance, it …
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Professor Philip Ogunbona
A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ‘‘lowlevel’’ features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ‘‘cognitive system.’’ The novelty of the model developed in this paper lies in the use of a self-organizing input layer …
Securing Wavelet Compression With Random Permutations, Takeyuki Uehara, Reihaneh Safavi-Naini, Philip Ogunbona
Securing Wavelet Compression With Random Permutations, Takeyuki Uehara, Reihaneh Safavi-Naini, Philip Ogunbona
Professor Philip Ogunbona
Wavelet compression for digital images achieves very high compression with reasonably high image quality and so is widely used in various applications. Adding security to compression algorithms has been proposed in a number of compression systems with the aim reducing the overall cost of compression and encryption. In this paper we propose a combined compression and encryption system based on wavelet transform and examine its security. Our results show that with a relatively small added cost varying degrees of security can be obtained while maintaining the performance of the compression system.
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
Professor Philip Ogunbona
A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, Fuzzy Adaptive Resonance Theory (Fuzzy ART), acts as the high level decision making and recognition system. Some modifications to the Fuzzy ART make it capable of simulating the post-natal and evolutionary development of the human visual system. The proposed system has been evaluated using …
Wavelet Thresholding In Partially Linear Models: A Computation And Simulation, Leming Qu
Wavelet Thresholding In Partially Linear Models: A Computation And Simulation, Leming Qu
Leming Qu
Partially linear models have a linear part as in the linear regression and a non-linear part similar to that in the non-parametric regression. The estimates in Partially Linear Models have been studied previously using traditional smoothing methods such as smoothing spline, kernel and piecewise polynomial smoothers. In this paper, a wavelet thresholding method for estimating the corresponding parameters in Partially Linear Models is presented. Extensive simulation results shows that wavelet smoothing approach is comparable to traditional smoothing methods when their assumptions are satisfied. But wavelet smoothing is often superior when assumptions about the smoothness of the underlying function of non-parametric …
Bayesian Wavelet Estimation Of Long Memory Parameter, Leming Qu
Bayesian Wavelet Estimation Of Long Memory Parameter, Leming Qu
Leming Qu
A Bayesian wavelet estimation method for estimating parameters of a stationary I(d) process is represented as an useful alternative to the existing frequentist wavelet estimation methods. The effectiveness of the proposed method is demonstrated through Monte Carlo simulations. The sampling from the posterior distribution is through the Markov Chain Monte Carlo (MCMC) easily implemented in the WinBUGS software package.
Wavelet Image Restoration And Regularization Parameters Selection, Leming Qu
Wavelet Image Restoration And Regularization Parameters Selection, Leming Qu
Leming Qu
For the restoration of an image based on its noisy distorted observations, we propose wavelet domain restoration by scale-dependent ∫1 penalized regularization method (WaveRSL1). The data adaptive choice of the regularization parameters is based on the Akaike Information Criterion (AIC) and the degrees of freedom (df) is estimated by the number of nonzero elements in the solution. Experiments on some commonly used testing images illustrate that the proposed method possesses good empirical properties.
Wavelet Reconstruction Of Nonuniformly Sampled Signals, Leming Qu, Partha S. Routh, Phil D. Anno
Wavelet Reconstruction Of Nonuniformly Sampled Signals, Leming Qu, Partha S. Routh, Phil D. Anno
Leming Qu
For the reconstruction of a nonuniformly sampled signal based on its noisy observations, we propose a level dependent l1 penalized wavelet reconstruction method. The LARS/Lasso algorithm is applied to solve the Lasso problem. The data adaptive choice of the regularization parameters is based on the AIC and the degrees of freedom is estimated by the number of nonzero elements in the Lasso solution. Simulation results conducted on some commonly used 1_D test signals illustrate that the proposed method possesses good empirical properties.
Rectification Of The Bias In The Wavelet Power Spectrum, Yonggang Liu, X. San Liang, Robert H. Weisberg
Rectification Of The Bias In The Wavelet Power Spectrum, Yonggang Liu, X. San Liang, Robert H. Weisberg
Yonggang Liu
This paper addresses a bias problem in the estimate of wavelet power spectra for atmospheric and oceanic datasets. For a time series comprised of sine waves with the same amplitude at different frequencies the conventionally adopted wavelet method does not produce a spectrum with identical peaks, in contrast to a Fourier analysis. The wavelet power spectrum in this definition, that is, the transform coefficient squared (to within a constant factor), is equivalent to the integration of energy (in physical space) over the influence period (time scale) the series spans. Thus, a physically consistent definition of energy for the wavelet power …