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Multifractal modelling of arbitrary object boundaries in image segmentation

Multifractal modelling of arbitrary object boundaries in ima...

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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 addresses the problem of approximation of arbitrary boundaries and edges resulting from image segmentation. Using MPEG-7 terminology, this problem can be considered as the search for a descriptor for a boundary feature of an object. The shape of an object is usually described by its boundaries, to differentiate it from the other regions of the image. Traditionally, Bezier curve fitting has been employed to model the boundaries of objects consisting of smooth and crisp curves. However, natural images contain complex shapes that are difficult to describe by such smooth-boundary modelling techniques. Consequently, we focus on fractals and multifractals to model arbitrary boundaries based on the singularity measure property of fractals. We use wavelets to identify boundary control points and reconstruct the self-similar boundaries with any fractal or multifractal dimension, using the midpoint displacement algorithm.

关键词： Fractals Image segmentation Image coding Shape Curve fitting Video compression MPEG 7 Standard Videoconference Image reconstruction Image storage

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Rotation sensitivity of independent component analysis to outliers

Rotation sensitivity of independent component analysis to ou...

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Canadian Conference on electrical and computer engineering (CCECE)

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

This paper evaluates the outlier sensitivity of five independent component analysis (ICA) algorithms (FastICA, extended-infomax, JADE, radical-ICA, and beta-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 beta-divergence is the least outlier-sensitive algorithm, followed by radical-ICA, FastICA (exponential and hyperbolic-tangent nonlinearities), extended-infomax, JADE, and FastICA (3rdand 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

关键词： Independent component analysis signal processing algorithms Higher order statistics Statistical analysis Robustness Performance analysis Pollution measurement Rotation measurement Source separation data compression

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Robust independent component analysis for cognitive informatics

Robust independent component analysis for cognitive informat...

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IEEE International Conference on Cognitive Informatics

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

This paper evaluates the outlier sensitivity of five independent component analysis (ICA) algorithms (FastICA, extended-Infomax, JADE, radical, and /spl beta/-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 /spl beta/-divergence is the least outlier-sensitive algorithm, followed by Radical, 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.

关键词： Robustness Independent component analysis Cognitive informatics Electroencephalography signal processing algorithms Statistical analysis Performance analysis Brain modeling Biomedical measurements Pollution measurement

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Characterizing chaos through Lyapunov metrics

Characterizing chaos through Lyapunov metrics

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IEEE International Conference on Cognitive Informatics

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

ISBN: (纸本)0769519865

Science, engineering, medicine, biology and many other areas deal with signals acquired in the form of time series from different dynamical systems for the purpose of analysis, diagnosis and control of the systems. The signals are often mixed with noise. Separating the noise from the signal may be very difficult if both the signal and the noise are broadband. The problem becomes inherently difficult when the signal is chaotic because its power spectrum is indistinguishable from a broadband noise. This paper describes how to measure and analyze chaos using Lyapunov metrics. The principle of characterizing strange attractors by the divergence and folding of trajectories is studied. A practical approach to evaluating the largest local and global Lyapunov exponents by rescaling and renormalization leads to calculating the m Lyapunov exponents for m-dimensional strange attractors either modeled explicitly (analytically), or reconstructed from experimental time-series data. Several practical algorithms for calculating Lyapunov exponents are summarized. The Lyapunov fractal dimension and Kolmogorov-Sinai and Renyi entropies are also described as they are related to the Lyapunov exponents.

关键词： Chaos Time series analysis engineering in medicine and biology Medical diagnostic imaging Systems biology Control system analysis signal analysis Medical control systems Biological control systems Control systems

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ECG signal compression and analysis in long-term monitoring

ECG signal compression and analysis in long-term monitoring

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Canadian Conference on electrical and computer engineering (CCECE)

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

This paper presents a moment-invariant compression technique for electrocardiogram (ECG) signals, applicable in a long-term (Holter) monitoring and analysis. It focuses on morphological cycle-to-cycle ECG data compression of a normal case. Experimental results show that the technique is suitable for real-time computation. Combined with Shannon-Fano encoding, the compression ratio achieves 21.74:1. The reconstruction error is very low, and performance is less affected by noise.

关键词： Electrocardiography signal analysis signal processing computerized monitoring data compression signal sampling Morphology data engineering Laboratories Image reconstruction

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Reduced-search fractal block coding of images

Reduced-search fractal block coding of images

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data compression Conference (DCC)

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

Summary form only given, as follows. Fractal based data compression has attracted a great deal of interest since Barnsley's introduction of iterated functions systems (IFS), a scheme for compactly representing intricate image structures. This paper discusses the incremental development of a block-oriented fractal coding technique for still images based on the work of Jacquin (1990). A brief overview of Jacquin's method is provided, and several of its features are discussed. In particular, the high order of computational complexity associated with the technique is addressed. This paper proposes that a neural network paradigm known as frequency sensitive competitive learning (FSCL) be employed to assist the encoder in locating fractal self-similarity within a source image. A judicious development of the proper neural network size for optimal time performance is provided. Such an optimally-chosen network has the effect of reducing the time complexity of Jacquin's original encoding algorithm from O(n/sup 4/) to O(n/sup 3/). In addition, an efficient distance measure for comparing two image segments independent of mean pixel brightness and variance is developed. This measure, not provided by Jacquin, is essential for determining the fractal block transformations. An implementation of fractal block coding employing FSCL is presented and coding results are compared with other popular image compression techniques. The paper also present the structure of the associated software simulator.

关键词： Fractals Block codes Image coding Neural networks Power capacitors data compression Computational complexity Frequency Image segmentation Pixel

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A unified approach to fractal dimensions

A unified approach to fractal dimensions

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IEEE International Conference on Cognitive Informatics

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

Many scientific papers treat the diversity of fractal dimensions as mere variations on either the same theme or a single definition. There is a need for a unified approach to fractal dimensions for there are fundamental differences between their definitions. This paper presents a new description of three essential classes of fractal dimensions based on: (i) morphology, (ii) entropy, and (iii) transforms, all unified through the generalized entropy-based Renyi fractal dimension spectrum. It discusses practical algorithms for computing 15 different fractal dimensions representing the classes. Although the individual dimensions have already been described in the literature, the unified approach presented in this paper is unique in terms of (i) its progressive development of the fractal dimension concept, (ii) similarity in the definitions and expressions, (iii) analysis of the relation between the dimensions, and (iv) their taxonomy. As a result, a number of new observations have been made, and new applications discovered. Of particular interest are behavioural processes such as dishabituation, irreversible and birth-death growth phenomena e.g., diffusion-limited aggregates, DLAs, dielectric discharges, and cellular automata, as well as dynamical nonstationary transient processes such as speech and transients in radio transmitters, multifractal optimization of image compression using learned vector quantization with Kohonen 's self-organizing feature maps (SOFMs), and multifractal-based signal denoising.

关键词： Fractals Morphology Entropy Taxonomy Aggregates Dielectrics Speech processing Radio transmitters Image coding Vector quantization

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Effects of ICA on the estimation of fractal sources

Effects of ICA on the estimation of fractal sources

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Canadian Conference on electrical and computer engineering (CCECE)

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

This paper presents a study of the effects of independent component analysis (ICA) on singularities in the blind source separation of fractal signals. Two equal power fractional Brownian noise signals of different fractal dimensions are synthesized, mixed using a nonsingular matrix, and separated using the extended-infomax ICA algorithm. The preservation of singularity features is measured by comparing the spectral fractal dimension of the sources to that of the identified independent components. Experiments show that ICA does not estimate the spectral dimension of the sources consistently. Neither does the spectral dimension follow the traditional Amari performance measure. ICA methods must be carefully administered if the measurement of fractality is important.

关键词： Independent component analysis Fractals Gaussian noise signal analysis Blind source separation signal processing algorithms signal processing 1f noise data compression Laboratories

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signal classification through multifractal analysis and complex domain neural networks

Signal classification through multifractal analysis and comp...

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IEEE International Conference on Cognitive Informatics

作者： W. Kinsner V. Cheung K. Cannons J. Pear T. Martin Department of Electrical and Computer Engineering Signal and Data Compression Laboratory Department of Psychology University of Manitoba Winnipeg MAN Canada

ISBN: (纸本)0769519865

This paper describes a system capable of classifying stochastic, self-affine, nonstationary signals produced by nonlinear systems. The classification and analysis of these signals is important because they are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the multifractal dimension domain, through the computation of the variance fractal dimension trajectory (VFDT). Features can then be extracted from the VFDT using a Kohonen self-organizing feature map. The second stage involves the use of a complex domain neural network and a probabilistic neural network to determine the class of a signal based on these extracted features. The results of this paper show that these techniques can be successful in creating a classification system which can obtain correct classification rates of about 87% when performing classification of such signals without knowing the number of classes.

关键词： Pattern classification Fractals signal analysis Neural networks signal processing Marine animals Stochastic systems Nonlinear systems Sampling methods data compression

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Image compression with singularity preservation

Image compression with singularity preservation

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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 and TRLabs University of Manitoba Winnipeg MAN Canada

This paper presents a new approach to lossy image compression through singularity preservation to obtain high compression ratios. The concept of this approach is based on a conjecture that image singularities carry most of the perceptual information, hence the essential part of an image should be represented by its singularity as opposed to its energy alone. Wavelet maxima have been chosen to represent signal singularity because of their ability to characterize image singularity fully. There are algorithms to reconstruct the original image faithfully from wavelet maxima. A compression scheme can then be designed to reduce the bit rate while preserving singularities. The resulting low bit-rate image has sharp edges without distortions, such as blockiness or blurs. This approach has been used to compress aerial ortho images, in which the perceptual quality of a 27 peak signal-to-noise ratio (PSNR) singularity-preserving image outperforms that of a 30 dB PSNR energy-preserving joint-photographic expert group (JPEG) image at a 15:1 compression ratio.

关键词： Image coding Image reconstruction PSNR Pixel Humans Transform coding Wavelet transforms Image storage data compression Laboratories

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