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AR model parameter estimation: from factor graphs to algorithms

AR model parameter estimation: from factor graphs to algorit...

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International Conference on Acoustics, speech, and signal processing (ICASSP)

作者： S. Korl H.A. Loeliger A.G. Lindgren Signal and Information Processing Laboratory ETH Zurich Switzerland Department of Electrical & Computer Engineering University of Rhode Island USA

The classic problem of estimating the parameters of an auto-regressive (AR) model is considered from a graphical model viewpoint. A number of practical parameter estimation algorithms - some of them well known, others apparently new - are derived as "summary propagation" in a factor graph. In particular, we demonstrate the joint estimation of AR coefficients, innovation variance, and noise variance.

关键词： Parameter estimation signal processing algorithms Technological innovation Graphical models Gaussian noise State-space methods signal processing Information processing Laboratories Error correction codes

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Power spectral density estimation and tracking of nonstationary pressure signals based on Kalman filtering

Power spectral density estimation and tracking of nonstation...

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Conference Proceedings - 26th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2004

作者： Aboy, M. McNames, J. Márquez, Óscar W. Hornero, R. Thong, T. Goldstein, B. Biomed. Signal Processing Laboratory Electrical and Computer Engineering Portland State University Portland United States Signal Theory Dept. ETSI-Telecomunicación University of Vigo Spain Signal Theory Dept. ETSI-Telecomunicación University of Valladolid Spain Biomedical Engineering Department Oregon Health and Science University Portland United States Pediatrics Department Oregon Health and Science University Portland United States

We describe an algorithm to estimate and track slow changes in power spectral density (PSD) of nonstationary pressure signals. The algorithm is based on a Kalman filter that adaptively generates an estimate of the autoregressive model parameters at each time instant. The algorithm exhibits superior PSD tracking performance in nonstationary pressure signals than classical nonparametric methodologies, and does not assume a piecewise stationary model of the data. Furthermore, it provides better time-frequency resolution, and is robust to model mismatches. We demonstrate its usefulness by a sample application involving PSD estimation and tracking of short records of simulated pressure waveforms. This algorithm is intended for applications were the PSD must be estimated and tacked during short transient periods, possibly after clinical interventions.

关键词： signal processing

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A nested spatial window-based approach to target detection for hyperspectral imagery

A nested spatial window-based approach to target detection f...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Weimin Liu Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780387422

A great challenge of hyperspectral target detection is to detect subtle targets without prior knowledge, particularly, when the targets of interest are insignificant and occur with low probabilities. This work provides a promising alternative to adaptive hyperspectral target detection. It considers a nested spatial window-based target detection (NSWTD) approach for hyperspectral imagery where a set of different spatial windows are nested and implemented to extract targets whose signatures are spectrally and spatially distinct. The use of nested spatial windows is determined by the image pixel resolution and applications. In order to demonstrate the performance of the proposed NSWTD approach, dual nested windows and three nested windows are implemented for computer simulations and real hyperspectral image experiments. The experimental results demonstrate that our proposed NSWTD approach performs effectively and improves a recent adaptive anomaly detector developed by Kwon et al. and the commonly used anomaly detector developed by Reed and Yu, referred to as RX algorithm. Its computation complexity is also very simple.

关键词： Object detection Hyperspectral imaging Hyperspectral sensors Detectors Spatial resolution Remote sensing signal processing Image processing Laboratories computer science

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A new application of texture unit coding to mass classification for mammograms

A new application of texture unit coding to mass classificat...

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IEEE International Conference on Image processing

作者： Yuan Chen Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780385543

Texture is one of important features of masses in mammograms. A recent texture unit-based texture spectrum approach, referred to as texture unit coding (TUC) has shown promise in texture classification. This paper presents a new application of the TUC to mass classification in mammograms. The TUC generates a texture spectrum for a texture image that can be used to describe the characteristics of masses. It also develops an information divergence (ID)-based discrimination criterion to measure the discrepancy between two texture spectra, a concept yet to explore in texture analysis. The TUC along with ID are further applied to mass classification in mammograms where the minimammographic database provided by the Mammographic Image Analysis Society (MIAS) is used for experiments.

关键词： Pixel Breast cancer Application software Image coding Image texture analysis Spatial databases Helium Image generation Histograms Shape

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New texture shape feature coding-based computer aided diagnostic methods for classification of masses on mammograms

New texture shape feature coding-based computer aided diagno...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Y. Chen C.-I. Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

This work presents new texture shape feature coding (TSFC)-based computer aided diagnostic (CAD) classification methods for classification of masses on mammograms. It introduces a new concept of 1.5-order 3-neighbor 3/spl times/3 connectivity to extract texture shape features that can describe multiples of 22.5/spl deg/. In order to effectively utilize these shape features, two new methods of implementing TFSC are further proposed to convert these features to texture feature numbers (TFNs), TFNs in quaternary (TFNq) which expresses a TFN in quaternary expansion and TFNs in product (TFNx ) that represents a TFN in terms of a product. Both TFNq and TFNx can then produce texture shape histograms in the same way that a gray-level histogram is generated for an image. Such a texture shape histogram is further used to generate various shape features of masses on mammograms for classification. In order to demonstrate the promise of our TSFC-based CAD methods, the MiniMammographic database provided by the Mammographic Image Analysis Society (MIAS) is used for experiments.

关键词： Shape Feature extraction Histograms Image coding Character recognition Remote sensing signal processing Image processing Laboratories computer science

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Factorized all-pass IIR adaptive notch filters

Factorized all-pass IIR adaptive notch filters

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International Conference on Acoustics, speech, and signal processing (ICASSP)

作者： J.E. Cousseau P.D. Donate Y. Liu Department of Electrical and Computer Engineering Universidad Nacional del Sur Bahia Blanca Argentina Signal Processing Laboratory Helsinki University of Technology Finland

A new family of IIR adaptive notch filters is presented. The family is based on a second-order factorization of the all-pass transfer function that forms the multiple notch filter. These realizations represent an extension of a previous ad-hoc scheme for adaptive notch filtering that avoids a high-order polynomial root finding in order to obtain the unknown frequencies of interest. An interesting aspect related to these novel algorithms is the fact that they introduce a different compromise between bias and SNR if compared with previous realizations available in the literature. Specifically, lower bias than in other approaches for low SNR can be achieved using the new realizations. This property is particularly attractive for multiple sinusoids estimation and tracking. In addition to the algorithm presentation, a discussion of the different properties and characteristics (stationary points, convergence) is included. Also, computer simulations are presented to illustrate the expected performance of the proposed adaptive filters.

关键词： Adaptive filters IIR filters Finite impulse response filter Frequency estimation Polynomials signal processing algorithms Poles and zeros Bandwidth Adaptive signal processing Laboratories

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Reducing impulsive noise in DSL systems - Robustness and delay

Reducing impulsive noise in DSL systems - Robustness and del...

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Proceedings of the Nordic signal processing Symposium (NORSIG)

作者： F.H. Gregorio J.E. Cousseau J.L. Figueroa Signal Processing Laboratory Helsinki University of Technology Finland Department of Electrical and Computer Engineering Universidad Nacional del Sur Bahia Blanca Argentina

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A uniform projection-based unsupervised classification for hyperspectral imagery

A uniform projection-based unsupervised classification for h...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jing Wang Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

ISBN: (纸本)0780387422

Orthogonal subspace projection (OSP) has received considerable interest in hyperspectral image classification. It performs well when the complete knowledge of image endmembers is available. In reality, obtaining such prior knowledge is very difficult, if not impossible. This work investigates a variant of OSP, called uniform projection (UP) and presents a UP-based unsupervised algorithm for hyperspectral image classification. It first implements a uniform target detector (UTD) to find a set of interfering signatures, I, then another uniform I-annihilated detector (UIAD) to find desired target signatures. The signatures in I are combined with the UIAD found desired signatures to form a target signatures matrix M which can be used for supervised image classification. In this case, OSP can be used for this purpose. In order to demonstrate the utility of the proposed unsupervised classification technique for hyperspectral imagery, a series of computer simulations and experiments are conducted for analysis.

关键词： Hyperspectral imaging Data mining Hyperspectral sensors Image classification Detectors Histograms Remote sensing signal processing Image processing Laboratories

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Action potential detection with automatic template matching

Action potential detection with automatic template matching

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： S. Kim J. McNames K. Burchiel Biomedical Signal Processing Laboratory Electrical and Computer Engineering Portland State University OR USA Department of Neurological Surgery Oregon Health and Sciences University OR USA

Detecting action potentials has an important role in analyzing extracellular neuronal recordings. Current algorithms require subjective tuning by a user in the form of user-specified parameters. This work describes a fully automatic template-matching spike detection algorithm that does not require any tuning. This algorithm is robust to noise and performs better than an optimum threshold detection algorithm.

关键词： Extracellular signal processing algorithms Detection algorithms Background noise Detectors Inspection Libraries Sampling methods Frequency Microelectrodes

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Development of digital image retrieval technique using autocorrelogram and wavelet based texture

Development of digital image retrieval technique using autoc...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： P. Skulsujirapa S. Aramvith S. Siddhichai Digital Signal Processing Research Laboratory Department of Electrical Engineering Faculty of Engineering Chulalongkom University Bangkok Thailand National Electronics and Computer Technology Center Pathumthani Thailand

In this paper, we present the development of a digital image retrieval technique using autocorrelogram and wavelet based texture feature. Autocorrelogram is a color feature that is able to incorporate the spatial correlation of alike color and is tolerant to large changes in appearance, color, contrast, and brightness. A wavelet transform is used to analyze the textures that are characterized by their energy distribution in each sub-band. We propose an integration of the autocorrelogram and wavelet based texture to improve the performance of the image retrieval system. Experimental results suggest that the proposed integration offers much better performance than using the features separately.

关键词： Digital images Image retrieval Image databases Histograms Spatial databases Wavelet transforms Feature extraction Indexing Content based retrieval Shape

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