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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms, Architectures, and Implementantions XVIII"

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Analytical approximations of translational subpixel shifts in signal and image registrations

Analytical approximations of translational subpixel shifts i...

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conference on advanced signal processing algorithms, architectures, and implementantions xviii

作者： Zhang, Qiang Wake Forest Univ Hlth Sci Biostat Sci Dept Winston Salem NC 27157 USA

ISBN: (纸本)9780819472946

Analytical approximations of translational subpixel shifts in both signal and image registrations are derived by setting the derivatives of a normalized cross correlation function to zero and solving them. Without the need of iterative searching, this methods achieves a complexity of only O(mn), given an image size of m x n. Without the need to upsample, computation memory is also saved. Tests using simulated signals and images show good results.

关键词： Subpixel image registration image superresolution

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A sharpness metric implementation for image processing applications with feedback

A sharpness metric implementation for image processing appli...

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conference on advanced signal processing algorithms, architectures, and implementantions xviii

作者： Lam, Eric P. Thales Rayheon Syst Battlefield Radar Fullerton CA USA

ISBN: (纸本)9780819472946

Some image processing applications require an image meet a quality metric before processing it. If an image is so degraded that it is difficult or impossible to reconstruct, the input image may be discarded. In this paper, we present a metric that measures the relative sharpness with respect to a reference image frame. The reference frame may be a previous input image or an output frame from the system. The sharpness metric is based on analyzing edges. The assumption of this problem is that input images are similar to each other in terms of observation angle and time.

关键词： image processing system closed-loop system sharpness metric edge frame

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Time-frequency analysis for the study of phase synchrony in the brain

Time-frequency analysis for the study of phase synchrony in ...

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conference on advanced signal processing algorithms, architectures, and implementantions xviii

作者： Aviyente, Selin Evans, Westley Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9780819472946

The quantification of synchrony is important for the study of large-scale interactions in the brain. Current synchrony measures depend on the energy of the signals rather than the phase, and cannot be reliably used as measures of neural synchrony. Moreover, the current methods are insufficient since they are limited to pairs of signals. These approaches cannot quantify the synchrony across a group of electrodes and over time-varying frequency regions. In this paper, we propose two new measures for quantifying the synchrony between both pairs and groups of electrodes using time-frequency analysis. The proposed measures are applied to electroencephalogram (EEG) data to quantify neural synchrony.

关键词： Phase measurement Time-frequency analysis Brain Electrodes Electroencephalography Mode locking Wavelets Iterated function systems signal analyzers Wavelet transforms

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Bandlimits and coherent modulation filtering of non-stationary signals

Bandlimits and coherent modulation filtering of non-stationa...

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conference on advanced signal processing algorithms, architectures, and implementantions xviii

作者： Clark, Pascal Atlas, Les Univ Washington Dept Elect Engn Seattle WA 98195 USA

ISBN: (纸本)9780819472946

Modulation filtering is a technique for filtering slowly-varying envelopes of frequency subbands of a non-stationary signal, ideally without affecting the signal's phase and fine-structure. Coherent modulation filtering is a potentially more effective subtype of such techniques where subband envelopes are determined through demodulation of the subband signal with a coherently detected subband carrier. In this paper we propose a coherent modulation filtering technique based on detecting the instantaneous frequency of a subband from its time-frequency representation. We show that coherent modulation filtering imposes a new bandlimiting constraint on the modulation product plus the ability to recover arbitrarily chosen envelopes and carriers from their modulation product. We show that a carrier estimate based on the time-varying spectral center-of-gravity satisfies the bandlimiting condition as well as Loughlin's previously derived bandlimiting constraint on the instantaneous frequency of carrier. These bandwidth constraints lead to effective and distortion-free modulation filters, offering new approaches for potential signal modification. The spectral center-of-gravity does not satisfy the condition on arbitrary recovery, however, which somewhat limits the flexibility of coherent modulation filtering. Demonstrations are provided with speech signals.

关键词： modulation time-frequency analysis time-varying filters bandwidth speech processing

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3D Object Matching on the GPU Using Spin-Image Surface Matching

3D Object Matching on the GPU Using Spin-Image Surface Match...

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conference on advanced signal processing algorithms, architectures, and implementantions xviii

作者： Davis, Nolan Braunreiter, Dennis Tebcherani, Cezario Tanida, Masatoshi Sci Applicat Int Corp San Diego CA 92121 USA

ISBN: (纸本)9780819472946

Spin-image surface matching is a technique for locating objects in a scene by processing three-dimensional surface information from sources such as light detection and ranging (LIDAR), structured light photography, and tomography. It is attractive for parallel processing on graphics processing units (GPUs) because the two main computational steps matching pairs of spin-images by correlation, and matching pairs of points between model and scene - are explicitly parallel. By implementing these parallel computations on the GPU, as well as recasting serial portions of the algorithm into a parallel form and structuring the algorithm to limit data exchanges between host and GPU, this project achieved an overall speedup of 20 times or more compared to conventional serial processing. A demonstration application has been developed that allows users to select among a set of models and scenes and then applies the spin-image surface matching algorithm to match the selected models to the scene. It also has several user interface controls for changing parameters. One new parameter is a geometric consistency ratio (GCR) that quantifies the matching performance and provides a measure for discarding low-quality matches. By toggling between GPU- and host-based processing, the application demonstrates the speedup achieved with parallelization on the GPU.

关键词： spin image surface matching object matching image correlation geometric consistency GPU graphical processing unit parallel processing speedup

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GPU Implementations for Fast Factorizations of STAP Covariance Matrices

GPU Implementations for Fast Factorizations of STAP Covarian...

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conference on advanced signal processing algorithms, architectures, and implementantions xviii

作者： Roeder, Michael Davis, Nolan Furtek, Jeremy Braunreiter, Dennis Healy, Dennis Sci Applicat Int Corp 10260 Campus Point Dr San Diego CA 92121 USA DARPA Microsyst Technol Off Arlington VA 22203 USA

ISBN: (纸本)9780819472946

One of the main goals of the STAP-BOY program has been the implementation of a, space-time adaptive processing (STAP) algorithm on graphics processing units (GPUs) with the goal of reducing the processing time. Within the context of GPU implementation, we have further developed algorithms that exploit data redundancy inherent in particular STAP applications. Integration of these algorithms with GPU architecture is of primary importance for fast algorithmic processing times. STAP algorithms involve solving a linear system in which the transformation matrix is a covariance matrix. A standard method involves estimating a covariance matrix from a data matrix, computing its Cholesky factors by one of several methods. and then solving the system by substitution. Some STAP applications have redundancy in successive data matrices from which the covariance matrices are formed. For STAP applications in which a data matrix is updated with the addition of a new data row at the bottom and the elimination of the oldest data in the top of the matrix, a sequence of data matrices have multiple rows in common. Two methods have been developed for exploiting this type of data redundancy when computing Cholesky factors. These two methods are referred to as 1) Fast QR factorizations of successive data matrices 2) Fast Cholesky factorizations of successive covariance matrices. We have developed GPU implementations of these two methods. We show that these two algorithms exhibit reduced computational complexity when compared to benchmark algorithms that do not exploit data, redundancy. More importantly, we show that when these algorithmic improvements are optimized for the GPU architecture, the processing times of a GPU implementation of these matrix factorization algorithms may be greatly improved.

关键词： Covariance cholesky QR factorization GPGPU Space Time Adaptive processing (STAP) block update CUDA thread parallelism block parallelism

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Separating Cognitive Processes with Principal Components Analysis of EEG Time-Frequency Distributions

Separating Cognitive Processes with Principal Components Ana...

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conference on advanced signal processing algorithms, architectures, and implementantions xviii

作者： Bernat, Edward M. Nelson, Lindsay D. Holroyd, Clay B. Gehring, William J. Patrick, Christopher J. Univ Minnesota Dept Psychol Minneapolis MN 55455 USA Univ Victoria Dept Psychol Victoria BC Canada Univ Michigan Dept Psychol Ann Arbor MI 48109 USA

ISBN: (纸本)9780819472946

Measurement of EEG event-related potential (ERP) data has been most commonly undertaken in the time-domain, which can be complicated to interpret when separable activity overlaps in time. When the overlapping activity has distinct frequency characteristics, however, time-frequency (TF) signal processing techniques can be useful. The current report utilized ERP data from a cognitive task producing typical feedback-related negativity (FRN) and P300 ERP components which overlap in time. TF transforms were computed using the binomial reduced interference distribution (RID), and the resulting TF activity was then characterized using principal components analysis (PCA). Consistent with previous work, results indicate that the FRN was more related to theta activity (3-7 Hz) and P300 more to delta activity (below 3 Hz). At the same time, both time-domain measures were shown to be mixtures of TF theta and delta activity, highlighting the difficulties with overlapping activity. The TF theta and delta measures, on the other hand, were largely independent from each other, but also independently indexed the feedback stimulus parameters investigated. Results support the view that TF decomposition can greatly improve separation of overlapping EEG/ERP activity relevant to cognitive models of performance monitoring.

关键词： Time-frequency analysis Principal component analysis Electroencephalography signal processing Brain Electrodes Psychology signal attenuation Time metrology Transform theory

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Superresolution imaging: a survey of current techniques

Superresolution imaging: a survey of current techniques

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conference on advanced signal processing algorithms, architectures, and implementantions xviii

作者： Cristobal, G. Gil, E. Sroubek, F. Flusser, J. Miravet, C. Rodriguez, F. B. CSIC Inst Opt Serrano 121 E-28006 Madrid Spain ASCR Inst Informat Theory & Automat Prague Czech Republic Univ Autonoma Madrid Escuela Politecn Super Madrid Spain

ISBN: (纸本)9780819472946

Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may, exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR, techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.

关键词： superresolution deconvolution energy minimization probabilistic neural network sequence processing

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