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compression of multifractal object boundaries: A comparison of two approaches

Compression of multifractal object boundaries: A comparison ...

引用

Canadian Conference on Electrical and Computer Engineering 2005

作者： Siddiqui, S. El-Boustani, A. Kinsner, W. Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg Man. R3T 5V6 Canada

This paper presents a comparative study of two approaches that address the problem of compression of arbitrary object boundaries resulting from image segmentation. The first approach uses multiresolution based techniques, in which the boundary is represented compactly by two sets of descriptors and control points which are obtained by applying wavelet and fractal analysis. The midpoint displacement algorithm is then applied to the two sets of control points in order to reconstruct the boundaries with the required fractal dimension. The second approach is based on the iterated function system (IFS). In this approach, the object boundary is regarded as an attractor of an IFS having the same Rényi fractal dimension spectrum as the original boundary. The experimental results show that the multifractal object boundaries can be modelled compactly using both approaches. The compression ratio achieved for the wavelet-based approach was 285:1, while for the IFS-based approach it was 52:1. These results can be compared with state of the art image compression techniques such as JPEG2000. © 2005 IEEE.

关键词： Image segmentation

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Surrogate ECG signals for the null-hypothesis analysis of heart monitoring

Surrogate ECG signals for the null-hypothesis analysis of he...

引用

Canadian Conference on Electrical and Computer Engineering 2005

作者： Potter, M. Kinsner, W. Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg Man. R3T 5V6 Canada

This paper introduces a class of synthetic electrocardiograms (ECG) for the purpose of testing the sensitivity and robustness of heart-monitoring techniques in controlled and reproducible simulations. These ECG surrogates are constructed by adapting an existing standard ECG synthesizer to act as a modulator on a tachogram input, enabling null-hypothesis analysis on heart-rate variability. This establishes an empirical link between the tachogram literature and the design of heart monitoring techniques. The new methodology is demonstrated experimentally and its performance is discussed. © 2005 IEEE.

关键词： Electrocardiography

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Rotation sensitivity of independent component analysis to outliers

Rotation sensitivity of independent component analysis to ou...

引用

Canadian Conference on Electrical and Computer Engineering 2005

作者： Gadhok, N. Kinsner, W. Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg Man. R3T 5V6 Canada

This paper evaluates the outlier sensitivity of five independent component analysis (ICA) algorithms (FastICA, Extended-Infomax, JADE, Radical-ICA, and β-divergence) using (i) the Amari separation performance index, (ii) the optimum angle of rotation error, and (iii) the contrast function difference, in an outlier-contaminated mixture simulation. The Amari separation performance index has revealed a strong sensitivity of JADE and FastICA (using 3rd- and 4th-order nonlinearities) to outliers. However, the two contrast measures demonstrated conclusively that β-divergence is the least outlier-sensitive algorithm, followed by Radical-ICA, FastICA (exponential and hyperbolic-tangent nonlinearities), Extended-Infomax, JADE, and FastICA (3rd- and 4th-order nonlinearities) in an outlier-contaminated mixture of two uniformly distributed signals. The novelty of this paper is the development of an unbiased optimization-landscape environment for assessing outlier sensitivity, as well as the optimum angle of rotation error and the contrast function difference as promising new measures for assessing the outlier sensitivity of ICA algorithms. © 2005 IEEE.

关键词： Independent component analysis

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Local and global analysis of multifractal singularity spectrum through wavelets

Local and global analysis of multifractal singularity spectr...

引用

Canadian Conference on Electrical and Computer Engineering 2005

作者： Faghfouri, A. Kinsner, W. Dept. of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg Man. R3T 5V6 Canada

This paper presents the implementation aspects pertinent to the computation of the multifractal singularity spectrum through wavelets, and the methods of overcoming them. Multifractals are mixtures of monofractals, and monofractals are self-affine objects that hold power law relationships over several scales. Such multifractals can be detected and measured through a singularity spectrum. Many natural and artificial phenomena such as turbulence, diffusion limited aggregates, and electrical discharges exhibit multifractality. Since these phenomena are highly nonlinear and nonstationary, regular analyses such as Fourier decomposition cannot characterize them effectively. In order to characterize, compare and quantify multifractal objects, appropriate measures such as the Rényi fractal dimension spectrum (RS) and Mandelbrot singularity spectrum (MS) are required. There are two major methods for calculating a singularity spectrum;one is through Legendre transform of the RS, and the other is through the wavelet transform modulus maxima (WTMM) method. This paper provides solutions to the difficulties that arise in the computation of the MS through WTMM, for one-dimensional signals and compares them to the existing multifractal literature and software implementations. Appropriate mother wavelets, continuous wavelet implementation, and thresholding of the wavelet coefficients are also discussed. © 2005 IEEE.

关键词： signal processing

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Singularity processing of nonstationary signals

Singularity processing of nonstationary signals

引用

Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： A. Langi W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba MAN Canada

This paper presents a new approach in processing nonstationary signals-such as speech signals and images-through singularity characterization. In this approach, we associate a singular measure /spl mu//sub f(t/) (r) with a transient at time t of a signal f(t) (where a real number r>0 is a time perturbation around t) and use the singularity behaviour of the measure for the characterization of the signal nonstationarity. The approach is capable of characterizing isolated transients through Holder exponents (or singularity strength), as well as mixture transients (e.g. singularity everywhere) through the concept of fractality and multifractality. The paper discusses the concept and the practicality of applying this approach to signals. The paper also shows that this approach can provide a unifying framework for previously published work on applying nonlinear, chaotic, fractal, and multifractal analysis to signals. We show that the main conceptual issue in applying fractality and multifractality to signals using this framework is the proper selection of signal measures.

关键词： signal processing Fractals signal analysis Speech analysis Chaos Image edge detection Image coding Speech recognition Image analysis Image texture analysis

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Secure and resilient data printed on paper

Secure and resilient data printed on paper

引用

Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： A. Denis W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

Transmission and printing of sensitive information requires both data security, and protection against random and burst errors. This paper describes a technique that achieves two objectives: secure and reliable transmission of information; and integrity of the original information printed on paper. Information printed on paper is prone to burst and random errors. Resilience of the information to such errors is obtained by introducing redundancy into the original data using forward error correcting codes. We have selected a concatenation of a Reed-Solomon (RS) code, interleaved with a self-orthogonal majority decodable convolutional code. To measure the performance of the concatenated code, the bit error rate against signal to noise ratio of the code is compared with codes of equivalent rate (e.g., RS code alone) and with the unencoded data. Security of the printed data can be achieved through encryption. We have selected a probabilistic encryption scheme of messages to achieve an increased security against reverse engineering of the printed pattern. A new document signature extraction scheme based on fractal signal processing is described. Such a signature is then included with the document in the security pattern.

关键词： data security Error correction codes Cryptography Printing Protection Resilience Redundancy Reed-Solomon codes Decoding Convolutional codes

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Adaptive wavelet subband coding for music compression

Adaptive wavelet subband coding for music compression

引用

data compression Conference (DCC)

作者： K. Ferens W. Kinsner Department of Electrical & Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper describes modelling of the coefficient domain in wavelet subbands of wideband audio signals for low-bit rate and high-quality compression. The purpose is to develop models of the perception of wideband audio signals in the wavelet domain. The coefficients in the wavelet subbands are quantized using a scheme that adapts to the subband signal by setting the quantization step size for a particular subband to a size that is inversely proportional to the subband energy, and then, within a subband, by modifying the energy determined step size as inversely proportional to the amplitude probability density of the coefficient. The amplitude probability density of the coefficients in each subband is modelled using learned vector/scalar quantization employing frequency sensitive competitive learning. The source data consists of 1-channel, 16-bit linear data sampled at 44.1 kHz from a CD containing major classical and pop music. Preliminary results show a bit-rate of 150 kbps, rather than 705.6 kbps, with no perceptual loss in quality. The wavelet transform provides better results for representing multifractal signals, such as wide band audio, than do other standard transforms, such as the Fourier transform.

关键词： Multiple signal classification Wavelet transforms Psychoacoustic models Wideband Frequency Fourier transforms signal resolution data compression Wavelet domain Fractals

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Power system transient modelling and classification

Power system transient modelling and classification

引用

Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： J. Chen W. Kinsner B. Huang Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper presents a method of modelling of power transients and their classification. A discrete wavelet transform and multifractal analysis based on a variance fractal dimension trajectory technique are used as tools to analyze the transients for feature extraction. A probabilistic neural network is used as a classifier for classification of transients associated with power system faults and switching. Experiments show that the classification system can achieve classification rate of 99% for power transients, and is robust in noisy environments.

关键词： Power system transients Power system modeling Discrete wavelet transforms Fractals Analysis of variance Transient analysis Wavelet analysis Feature extraction Neural networks Power system faults

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A study of microscopic images of human breast disease using competitive neural networks

A study of microscopic images of human breast disease using ...

引用

Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： R. Allan W. Kinsner Department of Electrical and Computer Engineering Signal & Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

ISBN: (纸本)0780367154

Competitive neural networks offer a unique opportunity to extract features from medical images objectively. An advantage of this approach is that medical image analysis could be automated or semi-automated. This automation could lead to improved precision and accuracy of diagnostic interpretation, while semi-automation could achieve much the same goal and would serve as a natural stepping-stone to full automation. This paper shows that all types of competitive neural networks can extract general features from images obtained through a microscope of four types of human breast disease, two benign and two malignant. Assessed qualitatively, the features broadly encompass thresholding and edge detection. These features are extracted regardless of supervision or lack of supervision. To visual inspection, there are no obvious sharp distinctions between benign and malignant diagnoses, the most important distinction in tissue diagnosis.

关键词： Microscopy Humans Breast Diseases Feature extraction Neural networks Biomedical imaging Medical diagnostic imaging Image edge detection Automation

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Modelling of multifractal object boundaries

Modelling of multifractal object boundaries

引用

Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： S. Siddiqui W. Kinsner Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

This paper presents a new technique that combines fractal and wavelet analyses to model rough (nonsmooth) but crisp (one-pixel wide) object boundaries that have fractal or multifractal characteristics. The boundary is represented compactly by two sets of descriptors and control points. The first set contains information about complexities present on the boundary and is calculated using a fractal dimension analysis. The second set contains information about the shape of the boundary and is calculated by using wavelet analysis. We apply the midpoint displacement algorithm on the two sets of control points in order to reconstruct boundaries with the required fractal or multifractal dimension. The quality of reconstruction is measured using the Renyi fractal dimension singularity measure. Experimental results produced compression ratios in the range of 300:1 to 450:1, while preserving the complexities of the original boundary, as measured by the above multifractal metrics.

关键词： Fractals Image coding Wavelet analysis Image segmentation Image reconstruction Shape Information analysis Video compression Curve fitting Image analysis

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