@inproceedings {299, title = {Prioritized spectrum sensing in cognitive radio based on spatiotemporal statistical fusion}, booktitle = {IEEE Wireless Communications \& Networking Conference}, year = {2009}, abstract = {

In this paper, a novel statistics-driven spectrum sensing algorithm is developed for improving spectrum sensing efficiency in the media access control (MAC) layer of cognitive radio (CR) systems. The proposed algorithm aims to achieve higher spectrum sensing efficiency and spectrum access opportunity by prioritizing channels for fine sensing based on the statistical likelihood of channel availability. The sensing priority is obtained by jointly exploiting the long-term spatiotemporal statistics recorded from the historical result of fine sensing, the short-term statistical information of channel condition obtained from a small-scale observation window, and the instantaneous statistical information obtained from fast sensing. Simulation results show that the proposed prioritization algorithm can achieve improved data transmission rates and reduced missed spectrum access opportunities when compared to the conventional non- prioritization spectrum sensing approach for situations where cooperative spectrum sensing is not suitable.

}, keywords = {access protocols, channel availability, cognitive radio, frequency allocation, MAC, media access control layer, prioritized spectrum sensing, spatiotemporal statistical fusion, statistical analysis, wireless channels}, doi = {http://dx.doi.org/10.1109/WCNC.2009.4917920}, author = {X Wang and A Wong and P-H Ho} } @inproceedings {300, title = {Stochastic channel prioritization for spectrum sensing in cooperative cognitive radio}, booktitle = {Consumer Communications and Networking Conference}, year = {2009}, abstract = {

In this paper, a novel cooperative stochastic channel prioritization algorithm is presented for the purpose of improving spectrum sensing efficiency in cooperative cognitive radio systems. The proposed algorithm achieves the goal by prioritizing the channels for fine sensing based on both local statistics obtained by the cognitive radio as well as long-term spatiotemporal statistics obtained from other cognitive radios. Channel priority is determined in a stochastic manner by performing statistical fusion on the local statistics and statistics from neighboring cognitive radios to obtain a biasing density from which stochastic sampling can be used to identify the likelihood of channel availability. Therefore, the individual cognitive radios collaborate to improve the likelihood of each cognitive radio in obtaining available channels. Simulation results show that the proposed cooperative stochastic channel prioritization algorithm can be used to reduce both sensing overhead and percentage of missed opportunities when implemented in a complimentary manner with existing cooperative cognitive radio systems.

}, keywords = {cognitive radio, cooperative cognitive radio, long-term spatiotemporal statistic, spectrum sensing efficiency, statistical analysis, statistical fusion, stochastic channel prioritization algorithm, stochastic processes, stochastic sampling}, url = {http://dx.doi.org/10.1109/CCNC.2009.4784893}, author = {X Wang and A Wong and P-H Ho} } @inproceedings {291, title = {Video pause detection using wavelets}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

As the volume of digital video captured and stored continues to increase, research efforts have focused on content management systems for video indexing and retrieval applications. A first step in generic video processing is shot boundary detection. This paper addresses a novel algorithm for abrupt shot (cut/pause) detection-especially on frames with similar statistics-based on the wavelet transform and content entropy. The algorithm has been successfully tested on some video categories including sport and live videos. Its quantitative performance has been compared to other known methods including pixel, histogram, frequency domain and statistics difference. In each test, the proposed wavelet method outperforms the others.

}, keywords = {content entropy, content management, content management system, digital video capture, entropy, indexing, shot boundary detection, statistical analysis, statistics-based wavelet transform, video indexing, Video Recording, video retrieval, wavelet transforms}, doi = {http://dx.doi.org/10.1109/CRV.2009.20}, author = {S Zaboli and D A. Clausi} } @inproceedings {340, title = {Statistical fusion and sampling of scientific images}, booktitle = {15th IEEE International Conference on Image Processing}, year = {2008}, abstract = {

Porous media are an important class of heterogeneous materials possessing complex random structures. Due to the limitation in measuring high resolution real samples, studying different physical properties of porous media requires the reconstruction of artificial samples. In many cases of significant interest, we have a two-scale reconstruction, in which only the large scales are resolved by low resolution measurements, leaving fine scales to be inferred from statistical models. In this paper we propose a statistical fusion approach for a two-scale porous media reconstruction, in which low resolution measurements are fused with high resolution samples, with synthetic realizations generated by posterior sampling.
}, keywords = {artificial samples, complex random structures, heterogeneous materials possessing, image fusion, image reconstruction, image resolution, image sampling, low resolution measurements, porous media, Posterior sampling, scientific image sampling, statistical analysis, statistical fusion approach, two-scale porous media reconstruction, two-scale reconstruction}, doi = {http://dx.doi.org/10.1109/ICIP.2008.4712004}, author = {A Mohebi and P Fieguth} } @inproceedings {362, title = {SAR sea ice image segmentation based on edge-preserving watersheds}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2007}, month = {05/2007}, address = {Montreal, Quebec, Canada}, abstract = {

This paper presents a hybrid method for the segmentation of SAR sea ice images, which consists of an initial watershed segmentation followed by a region merging. Iterative bilateral filtering is used to reduce speckle noise and suppress irrelevant image details, which can significantly alleviate oversegmentation of watersheds. Since edges are well preserved by bilateral filtering, the watershed algorithm is capable of precisely locating object boundaries. Final segmentation is accomplished by applying an iterative region merging on the watershed regions by taking into account local boundary strengths and regional statistics. The efficiency of the proposed method has been demonstrated on the segmentation of SAR sea ice images. In comparison with traditional watershed algorithm, our method achieves better performance in identifying filament structures such as leads.

}, keywords = {edge-preserving watersheds, filtering theory, image segmentation, iterative bilateral filtering, iterative methods, local boundary strengths, radar imaging, region merging, regional statistics, SAR sea ice image segmentation, speckle noise, statistical analysis, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2007.58}, author = {X Yang and D A. Clausi} } @inproceedings {393, title = {Filament preserving segmentation for SAR sea ice imagery using a new statistical model}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Modelling spatial context constraints using Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to SAR sea ice segmentation has also been demonstrated by Deng and Clausi (2005). However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and helpful for identifying certain ice types. A new statistical context model is proposed that can preserve such narrow elongated features while producing similar smooth segmentation results as those of existing MRF based approaches

}, keywords = {Feature Extraction, filament preserving segmentation, geophysical signal processing, Image Denoising, image segmentation, Markov processes, Markov random field, narrow elongated features, noisy image segmentation, oceanographic techniques, radar imaging, SAR sea ice imagery, sea ice, ship navigation, spatial context constraints, statistical analysis, statistical model, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/ICPR.2006.561}, author = {Q Yu and D A. Clausi} } @inproceedings {408, title = {A statistical thresholding method for cell tracking}, booktitle = {2006 IEEE International Symposium on Signal Processing and Information Technology}, year = {2006}, abstract = {

Tracking the motion of cells in culture is a task, which often still Is undertaken manually, and for which automated methods are strongly desirable. Researchers visually perform cell motion analysis, observe cell movements and cell shape changes for hours to discover when, where and how fast It moves, splits or dies. Hematopoletlc stem cells (HSCs) proliferate and differentiate to different blood cell types continuously during their lifetime, and are of substantial interest in gene therapy, cancer, and stem-cell research. In this paper a statistical method is introduced to track HSCs over time. A statistical thresholding method is combined with joint probabilistic data association in the proposed HSC tracker

}, keywords = {blood, blood cell, cancer, cell motion analysis, cell tracking, cellular biophysics, gene therapy, hematopoletlc stem cells proliferate, image motion analysis, image segmentation, medical image processing, statistical analysis, statistical thresholding method, stem-cell research}, doi = {http://dx.doi.org/10.1109/ISSPIT.2006.270801}, author = {J Ramunas and N Nezamoddin Kachouie and P Fieguth and E Jervis} } @inproceedings {789, title = {Average cell orientation, shape and size estimated from tissue images}, booktitle = {2nd Annual Canadian Conference on Computer and Robot Vision}, year = {2005}, month = {05/2005}, address = {Victoria, B.C., Canada}, abstract = {

Four computer vision algorithms to measure the average orientation, shape and size of cells in images of biological tissue are proposed and tested. These properties, which can be embodied by an elliptical \&$\#$39;composite cell\&$\#$39; are crucial for biomechanical tissue models. To automatically determine these properties is challenging due to the diverse nature of the image data, with tremendous and unpredictable variability in illumination, cell pigmentation, cell shape, and cell boundary visibility. First, a simple edge detection routine is performed on the raw images to locate cell edges and remove pigmentation variation. The edge map is then converted into the magnitude spatial-frequency domain where the spatial patterns of the cells appear as energy impulses. Four candidate methods that analyze the spatial-frequency data to estimate the properties of the composite cell are presented and compared. These methods are: least squares ellipse fitting, correlation, area moments and Gabor filters. Robustness is demonstrated by successful application on a wide variety of real images.

}, keywords = {area moments, average cell orientation, average cell shape, average cell size, biological tissue, biological tissues, biology computing, biomechanical tissue models, cell boundary visibility, cell geometry, cell pigmentation, Computer Vision, edge detection, elliptical composite cell, embryology, Gabor filters, least squares ellipse fitting, magnitude spatial-frequency domain, medical image processing, morphogenesis, shape detection, simple edge detection, statistical analysis, tissue images}, doi = {http://dx.doi.org/10.1109/CRV.2005.22}, author = {P Iles and D A. Clausi and S Puddister and G W. Brodland} } @inproceedings {420, title = {Correlated wavelet shrinkage: models of local random fields across multiple resolutions}, booktitle = {International Conference on Image Analysis and Recognition}, year = {2005}, abstract = {

This paper proposes a novel correlated shrinkage method based on wavelet joint statistics. Our objective is to demonstrate effectiveness of the wavelet correlation models [Z. Azimifar et al., 2004] in estimating the original signal from a noising observation. Simulation results are given to show the advantage of the new correlated shrinkage function. In comparison with the popular nonlinear shrinkage algorithms, it improves the denoised results.

}, keywords = {correlated wavelet shrinkage, Image Denoising, image resolution, image resolutions, local random fields, nonlinear shrinkage algorithms, statistical analysis, wavelet correlation models, wavelet joint statistics, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICIP.2005.1530352}, author = {Z Azimifar and P Fieguth and E Jernigan} } @article {418, title = {Statistical processing of large image sequences}, journal = {IEEE Transactions on Image Processing}, volume = {14}, year = {2005}, pages = {80 - 93}, abstract = {

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. We present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 times; 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Biological, Cluster Analysis, computational complexity, Computer Graphics, Computer Simulation, Computer-Assisted, correlation methods, correlation structure, dynamic estimation method, image enhancement, image interpretation, image sequences, Imaging, Information Storage and Retrieval, Kalman filter, Kalman filters, large-scale stochastic image sequence estimation, Models, Movement, Numerical Analysis, ocean surface temperature, Pattern Recognition, Remote Sensing, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, single-frame estimation, static estimation step, Statistical, statistical analysis, statistical image processing, stochastic processes, Subtraction Technique, Three-Dimensional, Video Recording}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2004.838703}, author = {F M. Khellah and P Fieguth and M J. Murray and M R. Allen} } @inproceedings {663, title = {Adaptive Wiener filtering of noisy images and image sequences}, booktitle = {IEEE International Conference on Image Processing}, year = {2003}, address = {Spain}, abstract = {

In this work, we consider the adaptive Wiener filtering of noisy images and image sequences. We begin by using an adaptive weighted averaging (AWA) approach to estimate the second-order statistics required by the Wiener filter. Experimentally, the resulting Wiener filter is improved by about 1 dB in the sense of peak-to-peak SNR (PSNR). Also, the subjective improvement is significant in that the annoying boundary noise, common with the traditional Wiener filter, has been greatly suppressed. The second, and more substantial, part of this paper extends the AWA concept to the wavelet domain. The proposed AWA wavelet Wiener filter is superior to the traditional wavelet Wiener filter by about 0.5 dB (PSNR). Furthermore, an interesting method to effectively combine the denoising results from both wavelet and spatial domains is shown and discussed. Our experimental results outperform or are comparable to state-of-art methods.

}, keywords = {adaptive filters, adaptive weighted averaging, adaptive Wiener filtering, Image Denoising, image sequences, noisy images, peak-to-peak SNR, second-order statistics, statistical analysis, wavelet domain, wavelet transforms, Wiener filters}, doi = {http://dx.doi.org/10.1109/ICIP.2003.1247253}, author = {F Jin and P Fieguth and L Winger and E Jernigan} } @inproceedings {757, title = {Hierarchical Markov models for wavelet-domain statistics}, booktitle = {2003 IEEE Workshop on Statistical Signal Processing}, year = {2003}, abstract = {

There is a growing realization that modeling wavelet coefficients as statistically independent may be a poor assumption. Thus, this paper investigates two efficient models for wavelet coefficient coupling. Spatial statistics which are Markov (commonly used for textures and other random imagery) do not preserve their Markov properties in the wavelet domain; that is, the wavelet-domain covariance Pw does not have a sparse inverse. The main theme of this work is to investigate the approximation of Pw by hierarchical Markov and non-Markov models.

}, keywords = {correlation methods, hierarchical Markov models, image processing, Markov processes, spatial statistics, statistical analysis, wavelet coefficient coupling, wavelet transforms, wavelet-domain covariance, wavelet-domain statistics}, doi = {http://dx.doi.org/10.1109/SSP.2003.1289393}, author = {Z Azimifar and P Fieguth and E Jernigan} } @inproceedings {598, title = {Towards random field modeling of wavelet statistics}, booktitle = {International Conference on Image Processing}, year = {2002}, address = {Rochester, NY}, abstract = {

The paper investigates the statistical characterization of signals and images in the wavelet domain. In particular, in contrast to common decorrelated-coefficient models, we find that the correlation between wavelet scales can be surprisingly substantial, even across several scales. We investigate possible choices of statistical-interaction models. One efficient and fast strategy which describes the wavelet-based statistical correlations is illustrated. Finally, the effectiveness of the proposed tool towards an efficient hierarchical MRF (Markov random field) modeling of within-scale neighborhoods and across-scale dependencies is demonstrated.

}, keywords = {image characterization, image processing, Markov processes, Markov random field modeling, MRF, signal characterization, statistical analysis, statistical image processing, wavelet statistics, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICIP.2002.1038035}, author = {Z Azimifar and P Fieguth and E Jernigan} } @article {524, title = {Multiply-rooted multiscale models for large-scale estimation}, journal = {IEEE Transactions on Image Processing}, volume = {10}, year = {2001}, pages = {1676 - 1686}, abstract = {

Divide-and-conquer or multiscale techniques have become popular for solving large statistical estimation problems. The methods rely on defining a state which conditionally decorrelates the large problem into multiple subproblems, each more straightforward than the original. However this step cannot be carried out for asymptotically large problems since the dimension of the state grows without bound, leading to problems of computational complexity and numerical stability. In this paper, we propose a new approach to hierarchical estimation in which the conditional decorrelation of arbitrarily large regions is avoided, and the problem is instead addressed piece-by-piece. The approach possesses promising attributes: it is not a local method-the estimate at every point is based on all measurements; it is numerically stable for problems of arbitrary size; and the approach retains the benefits of the multiscale framework on which it is based: a broad class of statistical models, a stochastic realization theory, an algorithm to calculate statistical likelihoods, and the ability to fuse local and nonlocal measurements

}, keywords = {asymptotically large problems, computational complexity, conditional decorrelation, estimation theory, geophysical signal processing, hierarchical estimation, image processing, large statistical estimation problems, large-scale estimation, local measurements fusion, multiply-rooted multiscale models, multiscale framework, nonlocal measurements fusion, numerical stability, oceanographic techniques, Remote Sensing, statistical analysis, statistical likelihoods, statistical models, stochastic realization theory}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.967396}, author = {P Fieguth} } @inproceedings {568, title = {Wavelet shrinkage with correlated wavelet coefficients}, booktitle = {International Conference on Image Processing}, year = {2001}, address = {Greece}, abstract = {

This paper investigates the statistical characterization of multiscale wavelet coefficients corresponding to random signals and images. Virtually all approaches to wavelet shrinkage model the wavelet coefficients as independent; we challenge that assumption and demonstrate several cases where substantial correlations may be present in the wavelet domain. In particular, the correlation between scales can be surprisingly substantial, even for pixels separated by several scales. Our goal is to develop an efficient random field model describing these statistical correlations, and demonstrate its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising

}, keywords = {Bayes methods, Bayesian wavelet shrinkage, correlated wavelet coefficients, Image Denoising, image resolution, multiscale wavelet coefficients, random field model, signal denoising, smoothing methods, statistical analysis, statistical correlations, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICIP.2001.958076}, author = {Z Azimifar and P Fieguth and E Jernigan} } @inproceedings {516, title = {Underground pipe cracks classification using image analysis and neuro-fuzzy algorithm}, booktitle = {IEEE Symposium on Intelligent Control}, year = {1999}, abstract = {

Pipeline surface defects such as cracks cause major problems for asset managers, particularly when the pipe is buried under the ground. The manual inspection of surface defects in the underground pipes has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection systems using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer asset managers an opportunity to significantly improve quality and reduce costs. A recognition and classification method for pipe cracks using image analysis and a neuro-fuzzy algorithm is proposed. In the pre-processing step, the cracks in the pipe are extracted from the homogenous background. Then, based on prior knowledge of cracks, five normalised features are extracted. In the classification step, a neuro-fuzzy algorithm is proposed that employs a trapezoidal fuzzy membership function and modified error backpropagation algorithm

}, keywords = {asset managers, automatic inspection systems, automatic optical inspection, backpropagation, civil engineering, eigenvalues and eigenfunctions, Feature Extraction, fuzzy neural nets, homogenous background, Hough transforms, image analysis, image classification, Mathematical morphology, modified error backpropagation algorithm, neuro-fuzzy algorithm, statistical analysis, surface defects, trapezoidal fuzzy membership function, underground pipe cracks classification}, doi = {http://dx.doi.org/10.1109/ISIC.1999.796688}, author = {S K. Sinha and F Karray and P Fieguth} } @inproceedings {473, title = {Computationally efficient multiscale estimation of large-scale dynamic systems}, booktitle = {International Conference on Image Processing}, year = {1998}, abstract = {

Statistical estimation of large-scale dynamic systems governed by stochastic partial differential equations is important in a wide range of scientific applications. However, the realization of computationally efficient algorithms for statistical estimation of such dynamic systems is very difficult. Conventional linear least squares methods are impractical for both computational and storage reasons. A previously-developed multiscale estimation methodology has been successfully applied to a number of large-scale static estimation problems. In this paper we apply the multiscale approach to the more challenging dynamic estimation problems, introducing a recursive procedure that efficiently propagates multiscale models for the estimation errors in a manner analogous to, but more efficient than, the Kalman filter\&$\#$39;s propagation of the error covariances. We illustrate our research in the context of 1-D and 2-D diffusive processes

}, keywords = {1-D diffusive process, 2-D diffusive processes, computationally efficient multiscale estimation, dynamic estimation, estimation errors, iterative methods, large-scale dynamic systems, partial differential equations, recursive estimation, recursive procedure, statistical analysis, statistical estimation, stochastic partial differential equations, stochastic processes}, doi = {http://dx.doi.org/10.1109/ICIP.1998.999018}, author = {T T. Ho and P Fieguth and A S. Willsky} } @inproceedings {474, title = {Hierarchical methods for global-scale estimation problems}, booktitle = {IEEE Canadian Conference on Electrical and Computer Engineering}, year = {1998}, address = {Waterloo, Canada}, abstract = {

There is a substantial signal-processing challenge associated with large-scale (especially global-scale) remote-sensing problems: solving the statistical inverse problem (i.e. deducing the properties of the sensed field from measurements) by brute force, that is by covariance matrix inversion, is completely impractical for fields involving millions of pixels. This paper reports on the ongoing development of an alternative technique, in which the statistical problem is modeled on a multiscale tree, applied to estimating sea-surface temperature (SST) based on infrared radiance observations from the along-track scanning radiometers (ATSRs)

}, keywords = {along-track scanning radiometers, covariance matrix inversion, geophysical signal processing, global-scale estimation problems, hierarchical methods, image processing, infrared radiance observations, inverse problems, measurements, multiscale tree, oceanographic techniques, parameter estimation, radiometry, Remote Sensing, remote-sensing, sea-surface temperature, sensed field, signal-processing, statistical analysis, statistical inverse problem, statistical problem, tree data structures}, doi = {http://dx.doi.org/10.1109/CCECE.1998.682707}, author = {P Fieguth and M R. Allen and M J. Murray} } @inproceedings {475, title = {Multipole-motivated reduced-state estimation}, booktitle = {International Conference on Image Processing}, year = {1998}, address = {Chicago}, abstract = {

This paper discusses efficient solutions to large-scale two-dimensional estimation problems, using reduced state methods motivated by the multipole method of mathematical physics. The work is mainly exploratory, building on past efforts in multiscale statistical signal modeling and estimation. We will illustrate applications to the estimation of Markov random field textures, with the motivation and goal the estimation of remotely-sensed fields

}, keywords = {image texture, large-scale 2D estimation problems, Markov processes, Markov random field textures, mathematical physics, multipole method, multipole-motivated reduced-state estimation, multiscale statistical signal estimation, multiscale statistical signal modeling, parameter estimation, reduced state methods, Remote Sensing, remotely-sensed fields, state estimation, statistical analysis}, doi = {http://dx.doi.org/10.1109/ICIP.1998.723580}, author = {P Fieguth} } @inproceedings {454, title = {Multiresolution statistical analysis and assimilation of large ocean data sets}, booktitle = {20th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {1995}, abstract = {

A significant problem in oceanographic remote sensing is the dense gridding or smoothing of sparsely sampled altimetric data. The smoothing of altimetric measurements has an application much broader than just the regular production of elevation maps for oceanographers, however. In particular, the ability to estimate ocean circulation patterns from altimetric data can serve as an important measure for the verification of ocean acoustic tomographic results. The authors present a multiscale technique capable of extremely efficient interpolation of altimetric data: about 256000 estimates and estimation error variances are computed in one minute on a Sun Sparc-10. They also demonstrate how similar techniques may be used to directly estimate the surface gradient and biases in the geoid-model error

}, keywords = {assimilation, biases, elevation maps, estimation error, geodesy, geoid-model error, geophysical signal processing, gridding, interpolation, large ocean data sets, multiresolution statistical analysis, multiscale technique, ocean acoustic tomographic results, ocean circulation patterns, oceanographic remote sensing, oceanographic techniques, parameter estimation, radar altimetry, radar signal processing, remote sensing by radar, signal resolution, smoothing, smoothing methods, sparsely sampled altimetric data, statistical analysis, surface gradient}, doi = {http://dx.doi.org/10.1109/ICASSP.1995.479422}, author = {P Fieguth and W C Karl and A S Willsky} }