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IEEE Workshop on Neural Networks for signal Processing

作者： R. Kretzschmar R. Bueler N.B. Karayiannis F. Eggimann Signal and Information Processing Laboratory Swiss Federal Institute of Technology Zurich Switzerland Department of Electrical and Computer Engineering University of Houston Houston TX USA

This study investigates the capacity of quantum neural networks (QNNs) to function as fuzzy classifiers. For this purpose, QNNs are compared with multilayer feedforward neural networks (FFNNs). The experiments are performed on two-dimensional speech data and investigate a variety of issues involved in the training of QNNs. This experimental study verifies that QNNs are capable of representing and quantifying the uncertainty inherent in the training data. It is also shown that simple post-processing of the QNN outputs makes QNNs an attractive alternative to conventional FFNNs for pattern classification applications.

关键词： Neural networks Feedforward neural networks Function approximation Fuzzy neural networks Multi-layer neural network Uncertainty Pattern classification Joining processes signal processing Information processing

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Object Segmentation Based on Multiple Features for Low Bit Rate Video Coding

Object Segmentation Based on Multiple Features for Low Bit R...

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2000年第16届世界计算机大会

作者： Datchakorn Tancharoen Somchai Jitapunkul Satien Triamlumlerd Panachit Kittipanya-ngam and Supattarachai Chompun Digital Signal Processing Research Laboratory Department of Electrical Engineering Chulalongkorn University National Electronics and Computer Technology Center

Video object segmentation for object based video coding according to MPEG-4 should be able to combine many features of image sequence to achieve high quality object *** significant features include color,motion vector and change *** paper presents the object segmentation algorithm which image features are combined to use in segmentation process.A fast recursive shortest spanning tree(FRSST) algorithm is used to segment image to be different intensity regions because of a good performance in image segmentation and a fast *** vectors are estimated from consecutive frames and searched by thresholding hierarchical block matching(HBM) which want quite low computation and give acceptable *** that many features will be integrated in segmentation decision ***,the postprocessing refines the final segmentation output. The results from some test sequences especially in video conference sequence have good performance and show interested-object boundary clearly.

关键词： object segmentation spanning tree image feature motion analysis

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Application of generalized constrained energy minimization approach to urban road detection

Application of generalized constrained energy minimization a...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chinsu Lin Chuin-Mu Wang Chein-I Chang Environmental Monitoring and Planning Laboratory Department of Forestry National Chiayi University Chiayi Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD USA

Classification of thematic layers have been used to update digital databases, particularly digital maps which can be accessed on the Internet by a fast growing number of users. One of the most important features in digital maps is traffic patterns and road networks. Unfortunately, due to low spectral and spatial resolutions of satellite imagery, many roads cannot be extracted at pixel level since the width of most urban and rural roads is less than the pixel size. In addition, road spectral signatures vary from pixel by pixel due to natural background. In this paper, an approach combining a subpixel detection method, called generalized constrained energy minimization with a principal component analysis-based fusion technique is proposed for urban road extraction. Experimental results show that the proposed method can effectively detect roads in Landsat TM and SPOT panchromatic images.

关键词： Roads Satellites Pixel Image databases Spatial databases Internet Telecommunication traffic Spatial resolution Minimization methods Remote sensing

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Unsupervised hyperspectral image analysis using independent component analysis

Unsupervised hyperspectral image analysis using independent ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shao-Shan Chiang Chein-I Chang I.W. Ginsberg Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA Department of Energy Remote Sensing Laboratories Las Vegas NV USA

In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the learning algorithm is able to converge to non-orthogonal independent components. This is particularly useful in hyperspectral image analysis since many materials extracted from a hyperspectral image may have similar spectral signatures and may not be orthogonal. The AVIRIS experiments have demonstrated that the proposed ICA provides an effective unsupervised technique for hyperspectral image classification.

关键词： Hyperspectral imaging Image analysis Independent component analysis Hyperspectral sensors Principal component analysis Vectors Laboratories Spectral analysis Pixel Composite materials

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A linear mixture analysis-based compression for hyperspectral image analysis

A linear mixture analysis-based compression for hyperspectra...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Q. Du Chein-I Chang D.C. Heinz M.L.G. Althouse I.W. Ginsberg Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA Department of Energy Remote Sensing Laboratories Las Vegas NV USA

The authors present a fully constrained least squares linear spectral mixture analysis-based compression technique for hyperspectral image analysis, particularly, target detection and classification. Unlike most compression techniques that directly deal with image gray levels, the proposed compression approach generates the abundance fractional images of potential targets present in an image scene and then encodes these fractional images so as to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in the abundance fractional images, the loss of information may have very little impact on image analysis. In some occasions, it even improves analysis performance. AVIRIS data experiments demonstrate that it can effectively detect and classify targets while achieving very high compression ratios.

关键词： Image analysis Image coding Hyperspectral imaging Hyperspectral sensors Layout Object detection Spectral analysis Performance analysis Feature extraction Pixel

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Enhanced waveform interpolative coding at 4 kbps 6

Enhanced waveform interpolative coding at 4 kbps

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6th IEEE Workshop on Speech Coding

作者： Gottesman, Oded Gersho, Allen Signal Compression Laboratory Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States

ISBN: (纸本)0780356519

This paper presents an enhanced waveform interpolative (EWI) speech coder at 4 kbps. The system incorporates novel features such as analysis-by-synthesis (AbS) vector-quantization (VQ) of the dispersion-phase, AbS optimization of the slowly evolving waveform (SEW), a special pitch search for transitions, and switched-predictive analysis-by-synthesis gain VQ. Subjective quality tests indicate that it exceeds that of MPEG-4 at 4 kbps and of G.723.1 at 5.3 kbps, and it is slightly better than that of G.723.1 at 6.3 kbps. © 1999 IEEE.

关键词： Vector quantization

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ENHANCED ANALYSIS-BY-SYNTHESIS WAVEFORM INTERPOLATIVE CODING AT 4 KBPS 6

ENHANCED ANALYSIS-BY-SYNTHESIS WAVEFORM INTERPOLATIVE CODING...

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6th European Conference on Speech Communication and Technology, EUROSPEECH 1999

作者： Gottesman, Oded Gersho, Allen Signal Compression Laboratory Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States

This paper presents an Enhanced analysis-by-synthesis (AbS) Waveform Interpolative (EWI) speech coder at 4 kbps. The system incorporates novel features such as: AbS quantization of the slowly evolving waveform (SEW), AbS vector quantization (VQ) of the dispersion phase, a special pitch search for transitions, and switched-predictive analysis-by-synthesis gain VQ. Subjective quality tests indicate that it exceeds MPEG-4 at 4 kbps and of G.723.1 at 5.3 kbps, and it is slightly better than G.723.1 at 6.3 kbps. © 1999 6th European Conference on Speech Communication and Technology, EUROSPEECH 1999. All rights reserved.

关键词： Vector quantization

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THE DETERMINISTIC ANNEALING APPROACH FOR DISCRIMINATIVE CONTINUOUS HMM DESIGN 6

THE DETERMINISTIC ANNEALING APPROACH FOR DISCRIMINATIVE CONT...

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6th European Conference on Speech Communication and Technology, EUROSPEECH 1999

作者： Gelin-Huet, Cecile Rose, Kenneth Rao, Ajit Signal Compression Lab Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States

We propose a deterministic annealing (DA) algorithm to design classifiers based on continuous observation hidden Markov models. The algorithm belongs to the class of minimum classification error (MCE) techniques that are known to outperform maximum likelihood (ML) design. Most MCE methods smooth the piecewise constant classification error cost to facilitate the use of local descent optimization methods, but are susceptible to the numerous shallow local minimum traps that riddle the cost surface. The DA approach employs randomization of the classification rule followed by minimization of the corresponding expected misclassification rate, while controlling the level of randomness via a constraint on the Shannon entropy. The effective cost function is smooth and converges to the MCE cost at the limit of zero entropy. The proposed algorithm significantly outperforms both standard ML and standard MCE design methods on the E-set database. © 1999 6th European Conference on Speech Communication and Technology, EUROSPEECH 1999. All rights reserved.

关键词： Maximum likelihood

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Perceptual zerotrees for scalable wavelet coding of wideband audio 6

Perceptual zerotrees for scalable wavelet coding of wideband...

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6th IEEE Workshop on Speech Coding

作者： Aggarwal, Ashish Cuperman, Vladimir Rose, Kenneth Gersho, Allen Signal Compression Lab. Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States

ISBN: (纸本)0780356519

This paper introduces a new algorithm for scalable coding of wideband audio signals. The technique is based on quantization of bi-orthogonal wavelet transformed coefficients using a perceptual zerotree method. An initial zerotree estimate of the wavelet coefficients is computed, followed by scalar quantization of the coefficients according to perceptual thresholds. The choice of wavelet decomposition and encoding parameters for each frame is adapted to the source characteristics employing a rate distortion criterion. The scalability of the coder is due to the tree structure, which enables graceful degradation with decrease in bit rate. Preliminary subjective tests indicate near-transparent quality for average bit rates in the range of 1.5 to 2.5 bits per sample. © 1999 IEEE.

关键词： Wavelet decomposition

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Kalman filtering approach to multispectral/hyperspectral image classification

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1999年第1期35卷 319-330页

作者： Chang, CI Brumbley, C Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Defense Fort Meade MD USA

Linear unmixing is a widely used remote sensing image processing technique for subpixel classification and detection where a scene pixel is generally modeled by a linear mixture of spectral signatures of materials present within the pixel. tin approach, called linear unmixing Kalman filtering (LUKF), is presented which incorporates the concept of linear unmixing into Kalman filtering so as to achieve signature abundance estimation, subpixel detection and classification for remotely sensed images. Zn this case, the linear mixture model used in linear unmixing is implemented as the measurement equation in Kalman filtering. The state equation which is required for Kalman filtering but absent in linear unmixing is then used to model the signature abundance. By utilizing these two equations the proposed LUKF not only can detect abrupt change in various signature abundances within pixels, but also can detect and classify desired target signatures. The performance of effectiveness and robustness of the LUKF is demonstrated through simulated data and real scene images, Satellite Pour l'Observation de la Terra (SPOT) and Hyperspectral Digital Imagery Collection (HYDICE) data.

关键词： Kalman filters Filtering Hyperspectral imaging Nonlinear filters Equations Hyperspectral sensors Layout Pixel Remote sensing Image processing

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