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Progressive dimensionality reduction for hyperspectral imagery

Progressive dimensionality reduction for hyperspectral image...

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Satellite Data Compression, Communication, and processing V

作者： Safavi, Haleh Liu, Keng-Hao Chang, Chein-I Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819477453

This paper develops to a new concept, called Progressive Dimensionality Reduction (PDR) which can perform data dimensionality progressive in terms of information preservation. Two procedures can be designed to perform PDR in a forward or backward manner, referred to forward PDR (FPDR) or backward PDR (BPDR) respectively where FPDR starts with a minimum number of spectral-transformed dimensions and increases the spectral-transformed dimension progressively as opposed to BPDR begins with a maximum number of spectral-transformed dimensions and decreases the spectral-transformed dimension progressively. Both procedures are terminated when a stopping rule is satisfied. In order to carry out DR in a progressive manner, DR must be prioritized in accordance with significance of information so that the information after DR can be either increased progressively by FPDR or decreased progressively by BPDR. To accomplish this task, Projection Pursuit (PP)-based DR techniques are further developed where the Projection Index (PI) designed to find a direction of interestingness is used to prioritize directions of Projection Index Components (PICs) so that the DR can be performed by retaining PICs with high priorities via FPDR or BPDR. In the context of PDR, two well-known component analysis techniques, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) can be considered as its special cases when they are used for DR. © 2009 SPIE.

关键词： Principal component analysis

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Mixed projection pursuit-based dimensionality reduction

Mixed projection pursuit-based dimensionality reduction

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Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV

作者： Safavi, Haleh Chang, Chein-I Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819476005

Projection Pursuit (PP) is a component transform technique which seeks a component whose projection vector points to a direction of interestingness in data space which can be specified by a Projection Index (PI). Two most popular component analysis-based techniques, Principal Components Analysis (PCA), Independent Component Analysis (ICA) can be considered as special cases with their PIs specified by data variance and statistical independency *** the fact that various component analysis-based techniques have been used for Dimensionality Reduction (DR) the components are generally generated by a specific technique. Even in the case of PP, the same PI has been used to generate project components. This paper explores the utility of PP in DR where various projection indexes are used for DR in context of PP. It further lays out a general setting for PP-based DR and develops algorithms to perform one dimension reduction at a time by using different PIs. In order to substantiate our findings, experiments are conducted to demonstrate advantages of the PP with mixed PIs-based DR over traditional PCA-based, ICA-based and PP-based DR techniques.©2009 SPIE.

关键词： Principal component analysis

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Causal pixel purity index (PPI)

Causal pixel purity index (PPI)

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Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV

作者： Wu, Chao-Cheng Chang, Chein-I Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819476005

Pixel Purity Index (PPI) has been widely used in endmember extraction. While it is available in ENVI software there are several interesting issues arising in its implementation. This paper re-invents the wheel by re-visiting the design rationale of the PPI and re-designing algorithms to implement PPI. More specifically, it develops the so-called Causal PPI (CPPI) which implements the PPI in a causal manner in the sense that the information used for data processing is only up to the data sample currently being visited. If the time required for computer processing is negligible, the CPPI actually becomes a real time PPI. The proposed CPPI can be implemented automatically and resolves the main issue of requiring human intervention to determine parameters.©2009 SPIE.

关键词： Pixels

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Soft decision-made hyperspectral measures for target discrimination and classification

Soft decision-made hyperspectral measures for target discrim...

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Imaging Spectrometry XIV

作者： Wu, Chao-Cheng Chang, Chein-I Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819477477

Many hyperspectral measures such as Spectral Angle Mapper (SAM), Euclidean Distance (ED), Spectral Information Divergence (SID) calculate values that can be used to measure the closeness between two hyperspectral signatures in terms of spectral similarity. When a hyperspectral measure is used for target discrimination or classification, a commonly used approach assigns a data sample to a spectral signature with the maximum spectral similarity or a class whose mean is most similar to its spectral signature. As a result, such a hyperspectral measure is called a hard decision hyperspectral measure and its performance is evaluated by its confusion matrix. This paper develops a new class of hyperspectral measures, called soft-decision hyperspectral measures which use the similarity value between a data sample and a target signature or a class as an indicator of the likelihood of the data sample assigned to this particular signature or class instead of signature or class map resulting from hard decisions. In order for a soft-decision to perform target discrimination or classification, the soft-decision hyperspectral measure-generated likelihood values are normalized to probabilities so that a threshold can be used to make hard decisions via a recently developed 3D ROC analysis. Experimental study demonstrates that these two approaches yield different results. © 2009 SPIE.

关键词： Classification (of information)

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A robust face detection method using intrinsic images and an energy minimization technique under the near-IR light spectrum

A robust face detection method using intrinsic images and an...

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2009 International Conference on Image processing, computer Vision, and Pattern Recognition, IPCV 2009

作者： Lahlou, Mouncef Adjouadi, Malek Barreto, Armando Center for Advanced Technology and Education Department of Electrical and Computer Engineering Florida International University Miami FL United States Digital Signal Processing Laboratory Department of Electrical and Computer Engineering Florida International University Miami FL United States

ISBN: (纸本)9781601321190

Detecting faces accurately under natural lighting conditions remains a challenging task for advancing the performance of face recognition systems. Most face detection algorithms are associated with visible spectrum imagery which compromises their efficiency when image acquisition is subject to variable illumination. In this study, we propose a simple yet powerful solution that uses multiple cues to detect and locate faces accurately in near infrared (NIR) imaging systems. Specific objectives include (I) extract optimally the illumination and reflectance components of the original image using an intrinsic images decomposition technique, (2) modify the segmentation process by using an energy minimization procedure in an adaptive fashion, and (3) propose an enhanced face detection process on the basis of pupil localization. The evaluation of our face detector on image sequences of 45 different subjects from the CBSR NIR face dataset demonstrated a highly competitive accuracy with less than 5% error rate.

关键词： Face recognition

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Multimodal speaker localization from omnidirectional videos

Multimodal speaker localization from omnidirectional videos

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17th European signal processing Conference, EUSIPCO 2009

作者： Reuse, Pascal Gurban, Mihai Austvoll, Ivar Thiran, Jean-Philippe Signal Processing Laboratory 5 Ecole Polytechnique Fédéralè de Lausanne Lausanne Switzerland Department of Electrical and Computer Engineering University of Stavanger Stavanger Norway

The use of omnidirectional cameras for videoconferencing promises to simplify the hardware setup necessary for large groups of participants. We investigate the use of a multimodal speaker detection algorithm on audio-visual sequences captured with such a camera, in particular, an algorithm that uses the audio energy together with the optical flow. We analyze several types of optical flow methods to determine the one which is appropriate to the omnidirectional context. © EURASIP, 2009.

关键词： Optical flows

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On the Convergence of Perturbed Non-Stationary Consensus Algorithms

On the Convergence of Perturbed Non-Stationary Consensus Alg...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and communications Societies

作者： T. C. Aysal K. E. Barner Communications Research in Signal Processing Group School of Electrical and Computer Engineering Cornell University Ithaca NY USA Signal Processing and Communications Group Electrical and Computer Engineering Department University of Delaware Newark DE USA

ISBN: (纸本)9781424435128

We consider consensus algorithms in their most general setting and provide conditions under which such algorithms are guaranteed to converge, almost surely, to a consensus. Let {A(t),B(t)} isin R N times N be (possibly) random, non-stationary matrices and {x(t),m(t)} isin R N times 1 be state and perturbation vectors, respectively. For any consensus algorithm of the form x(t + 1) = A(t)x(t) + B(t)m(t), we provide conditions under which consensus is achieved almost surely, i.e., Pr {lim trarrinfin x(t) = c1} = 1 for some c isin R. Moreover, we show that this general result subsumes recently reported results for specific consensus algorithms classes, including sum-preserving, non-sum-preserving, quantized and noisy gossip algorithms. Also provided are the e-converging time for any such converging iterative algorithm, i.e., the earliest time at which the vector x(t) is e close to consensus, and sufficient conditions for convergence in expectation to the initial node measurements average.

关键词： Convergence signal processing algorithms Iterative algorithms Peer to peer computing Computational modeling communications Society Sufficient conditions Time measurement Ad hoc networks Application software

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Perturbation analysis of whitening-rotation-based semi-blind MIMO channel estimation

Perturbation analysis of whitening-rotation-based semi-blind...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Feng Wan Wei-Ping Zhu M.N.S. Swamy Centre of Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

The whitening-rotation (WR)-based semi-blind methods have been shown to achieve much better channel estimation performance than the conventional training-based methods for MIMO systems. In this paper, the performance analysis is conducted for an ideal WR-based method, in which the knowledge of the ideal whitening matrix is known a priori. This analysis is a valuable study since the ideal WR-based method provides the upper bound of the channel estimation performance for WR-based methods. First, the expression of the error of the rotation matrix is derived by using a perturbation analysis. This result is then utilized to derive a closed-form expression of mean square error (MSE) of the ideal WR-based method. The simulation studies have shown that the theoretical MSE values are consistent with the simulation results, confirming the high accuracy of the derivation of the MSE expression.

关键词： MIMO Channel estimation Receiving antennas signal processing algorithms Performance analysis Mean square error methods Maximum likelihood estimation Transmitting antennas signal processing Upper bound

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A semi-blind algorithm for most significant tap detection in channel estimation of OFDM systems

A semi-blind algorithm for most significant tap detection in...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Feng Wan Wei-Ping Zhu M.N.S. Swamy Centre of Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

ISBN: (纸本)9781424438273

In this paper, a semi-blind algorithm is proposed for the detection of most significant tap (MST) in the sparse channel estimation of OFDM systems. Based on an analysis of the second-order statistics of the signal received through a noise-free sparse channel, a direct relationship between the positions of the most significant taps (MST) of the sparse channel and the lags of the nonzero correlation functions is revealled, leading to an efficient semi-blind MST detection algorithm. By using the acquired MST position, a sparse least square channel estimate is then obtained. A number of computer simulation-based experiments are carried out to confirm the effectiveness of the proposed semi-blind MST detection algorithm and the associated sparse LS channel estimation method.

关键词： Channel estimation OFDM signal processing algorithms Delay estimation Detection algorithms signal analysis Algorithm design and analysis Statistical analysis Delay lines Least squares approximation

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A spectral matching method for pitch estimation from noise-corrupted speech

A spectral matching method for pitch estimation from noise-c...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： C. Shahnaz W. -P. Zhu M. O. Ahmad Centre of Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

ISBN: (纸本)9781424438273

An effective method for pitch estimation from severely noise-corrupted speech is presented in this paper. We argue that an accurate estimate of a pitch-harmonic (PH) can be obtained from the Discrete Cosine Transform (DCT) power spectrum of an enhanced frame. By exploiting the PH thus extracted, a spectral matching method is developed in the DCT domain to determine the true harmonic number associated to the PH that leads to a pitch estimate. In a heavy noisy scenario, the superior efficacy of the proposed pitch estimation method relative to some of the existing ones is confirmed through simulation results using the Keele database.

关键词： Speech enhancement Discrete cosine transforms Noise reduction Speech coding Speech synthesis Noise robustness Autocorrelation White noise Additive noise Low pass filters

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