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Stochastic Search for signal processing Algorithm Optimization 01

Stochastic Search for Signal Processing Algorithm Optimizati...

引用

2001 ACM/IEEE conference on Supercomputing, SC 2001

作者： Singer, Bryan Veloso, Manuela Computer Science Department Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)158113293X

This paper presents an evolutionary algorithm for searching for the optimal implementations of signal transforms and compares this approach against other search techniques. A single signal processing algorithm can be represented by a very large number of different but mathematically equivalent formulas. When these formulas are implemented in actual code, unfortunately their running times differ significantly. signal processing algorithm optimization aims at finding the fastest formula. We present a new approach that successfully solves this problem, using an evolutionary stochastic search algorithm, STEER, to search through the very large space of formulas. We empirically compare STEER against other search methods, showing that it notably can find faster formulas while still only timing a very small portion of the search space. © 2001 ACM.

关键词： Evolutionary algorithms

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Computationally efficient strategies to perform anomaly detection in hyperspectral images

Computationally efficient strategies to perform anomaly dete...

引用

conference on Image and signal processing for Remote Sensing xVIII

作者： Rossi, Alessandro Acito, Nicola Diani, Marco Corsini, Giovanni Univ Pisa Dipartimento Ingn Informaz Via G Caruso 16 I-56122 Pisa Italy Accad Navale Livorno I-57100 Livorno Italy

ISBN: (纸本)9780819492777

In remote sensing, hyperspectral sensors are effectively used for target detection and recognition because of their high spectral resolution that allows discrimination of different materials in the sensed scene. When a priori information about the spectrum of the targets of interest is not available, target detection turns into anomaly detection (AD), i.e. searching for objects that are anomalous with respect to the scene background. In the field of AD, anomalies can be generally associated to observations that statistically move away from background clutter, being this latter intended as a local neighborhood surrounding the observed pixel or as a large part of the image. In this context, many efforts have been put to reduce the computational load of AD algorithms so as to furnish information for real-time decision making. In this work, a sub-class of AD methods is considered that aim at detecting small rare objects that are anomalous with respect to their local background. Such techniques not only are characterized by mathematical tractability but also allow the design of real-time strategies for AD. Within these methods, one of the most-established anomaly detectors is the Rx algorithm which is based on a local Gaussian model for background modeling. In the literature, the Rx decision rule has been employed to develop computationally efficient algorithms implemented in real-time systems. In this work, a survey of computationally efficient methods to implement the Rx detector is presented where advanced algebraic strategies are exploited to speed up the estimate of the covariance matrix and of its inverse. The comparison of the overall number of operations required by the different implementations of the Rx algorithms is given and discussed by varying the Rx parameters in order to show the computational improvements achieved with the introduced algebraic strategy.

关键词： hyperspectral images anomaly detection Rx algorithm real-time processing

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Translation, rotation and scaling invariant object and texture classification using polyspectra

Translation, rotation and scaling invariant object and textu...

引用

advanced signal-processing algorithms, architectures, and implementations

作者： Tsatsanis, Michail K. Giannakis, Georgios B. Univ of Virgina Charlottesville United States

The problem addressed in this paper is the detection and classification of deterministic objects and random textures in a noisy scene. An energy detector is developed in the cumulant domain, by exploiting the noise insensitivity of higher-order statistics. An efficient implementation of this detector is described, using matched filtering. Its performance is analyzed using asymptotic distributions in a binary hypothesis testing framework. Object and texture classifiers are derived using higher-order statistics. They are minimum distance classifiers in the cumulant domain, and can be efficiently implemented using a bank of matched filters. Further, they are robust to additive Gaussian noise and insensitive to object shifts. Extensions, which can handle object rotation and scaling are also discussed. An alternate texture classifier is derived from a ML viewpoint, that is more efficient at the expense of complexity. The application of these algorithms to texture modeling is shown and consistent parameter estimators are obtained. Simulations are shown for both the object and the texture classification problems.

关键词： Image processing

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Time-varying higher order spectra

Time-varying higher order spectra

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advanced signal processing algorithms, architectures, and implementations II

作者： Boashash, Boualem O'Shea, Peter Queensland Univ. of Technology Brisbane Queensland Australia

ISBN: (纸本)0819406945

A general solution for the problem of time-frequency signal representation of nonlinear FM signals is provided, based on a generalization of the Wigner-Ville distribution. The Wigner-Ville distribution (WVD) is a second order time-frequency representation. That is, it is able to give ideal energy concentration for quadratic phase signals and its ensemble average is a second order time-varying spectrum. The same holds for Cohen's class of time-frequency distributions, which are smoothed versions of the WVD. The WVD may be extended so as to achieve ideal energy concentration for higher order phase laws, and such that the expectation is a time-varying higher order spectrum. The usefulness of these generalized Wigner-Ville distributions (GWVD) is twofold. Firstly, because they achieve ideal energy concentration for polynomial phase signals, they may be used for optimal instantaneous frequency estimation. Second, they are useful for discriminating between nonstationary processes of differing higher order moments. In the same way that the WVD is generalized, we generalize Cohen's class of TFDs by defining a class of generalized time-frequency distributions (GTFDs) obtained by a two dimensional smoothing of the GWVD. Another results derived from this approach is a method based on higher order spectra which allows the separation of cross-terms and auto- terms in the WVD.

关键词： signal Theory

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Minimum-phase LU factorization preconditioner for Toeplitz matrices

Minimum-phase LU factorization preconditioner for Toeplitz m...

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advanced signal processing algorithms, architectures, and implementations II

作者： Ku, Ta-Kang Kuo, C.-C.J. Univ. of Southern California Los Angeles CA USA

ISBN: (纸本)0819406945

A new preconditioner is proposed for the solution of an N × N Toeplitz system TNx = b, where TN can be symmetric indefinite or nonsymmetric, by preconditioned iterative methods. The preconditioner FN is obtained based on factorizing the generating function T(z) into the product of two terms corresponding, respectively, to minimum-phase causal and anticausal systems and therefore called the minimum-phase LU (MPLU) factorization preconditioner. Due to the minimum-phase property, F N-1 is bounded. For rational Toeplitz TN with generating function T(z) = A(z-1)/B(z-1) + C(z)/D(z), where A(z), B(z), C(z), and D(z) are polynomials of orders p1, q1, p2, and q2, we show that the eigenvalues of FN-1TN are repeated exactly at 1 except at most α F outliers, where αF depends on p1, q1, p2, q2, and the number approximately ega of the roots of T(z) = A(z-1)D(z) + B(z-1)C(z) outside the unit circle. A preconditioner KN in circulant form generalized from the symmetric case is also presented for comparison.

关键词： signal processing

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Adaptive Lanczos methods for recursive condition estimation

Adaptive Lanczos methods for recursive condition estimation

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advanced signal-processing algorithms, architectures, and implementations

作者： Ferng, William R. Golub, Gene H. Plemmons, Robert J. NC State Univ Raleigh United States

Estimates for the condition number of a matrix are useful in many areas of scientific computing, including: recursive least squares computations, optimization, eigenanalysis, and general nonlinear problems solved by linearization techniques where matrix modification techniques are used. The purpose of this paper is to propose an adaptive Lanczos estimator scheme, which we call ale, for tracking the condition number of the modified matrix over time. Applications to recursive least squares (RLS) computations using the covariance method with sliding data windows are considered. ale is fast for relatively small n - parameter problems arising in RLS methods in control and signal processing, and is adaptive over time, i.e., estimates at time t are used to produce estimates at time t + 1. Comparisons are made with other adaptive and non-adaptive condition estimators for recursive least squares problems. Numerical experiments are reported indicating that ale yields a very accurate recursive condition estimator.

关键词： signal processing

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Redundant finite rings for fault-tolerant signal processors

Redundant finite rings for fault-tolerant signal processors

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advanced signal processing: algorithms, architectures, and implementations V

作者： Jullien, Graham A. Bizzan, S.S. Wigley, N.M. Miller, W.C. Univ. of Windsor Windsor ON Can

ISBN: (纸本)0819416207

Redundant Residue Number Systems (RRNS) have been proposed as suitable candidates for fault tolerance in compute intensive applications. The redundancy is based on multiple projections to moduli sub-sets and conducting a search for results that lie in a so-called illegitimate range. This paper presents RRNS fault tolerant procedures for a recently introduced finite polynomial ring mapping procedure (modulus replication RNS). The mapping technique dispenses with the need for many relatively prime ring moduli, which is a major draw-back with conventional RRNS systems. Although double, triple, and quadrupole modular redundancy can be implemented in the polynomial mapping structure, polynomial coefficient circuitry, or the independent direct product ring computational channels, for error detection and/or correction, this paper discusses the implementation of redundant rings which are generated by (1) redundant residues, (2) spare general computational channels, or (3) a combination of the two. The first architecture is suitable for RNS embedding in the MRRNS, and the second for single moduli mappings. The combination architecture allows a trade-off between the two extremes. The application area is in fault tolerant compute intensive DSP arrays.

关键词： Fault tolerant computer systems

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Towards image denoising in the latent space of learning-based compression 44

Towards image denoising in the latent space of learning-base...

引用

conference Applications of Digital Image processing xLIV

作者： Testolina, Michela Upenik, Evgeniy Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

In recent years, learning-based image compression has demonstrated similar or superior performance when compared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-based image compression takes advantage of autoencoders, which are architectures consisting of two main parts: a multi-layer neural network encoder and its dual decoder. The encoder maps the input image represented in the pixel domain to a compact representation, also known as latent space. Consequently, the decoder reconstructs the original image in the pixel domain from its latent representation, as accurately as possible. Traditionally, image processing algorithms, and in particular image denoising, are applied to images in the pixel domain before compression, and eventually in some cases as a post-processing stage after decompression. In this context, the combination of denoising operations with the autoencoder might reduce the computational cost while achieving similar performance in accuracy. In this paper, the idea of combining the image denoising task with compression is examined. In particular, the integration of denoising convolutional layers in the decoder of a learning-based compression network is investigated. Results show that, while the rate-distortion performance of the method is slightly reduced, a gain in the computational complexity can be achieved.

关键词： Image denoising learning-based compression compressed domain denoising latent space image processing

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Exploring Resolution and Degradation Clues as Self-supervised signal for Low Quality Object Detection 17th

Exploring Resolution and Degradation Clues as Self-supervise...

引用

17th European conference on Computer Vision (ECCV)

作者： Cui, Ziteng Zhu, Yingying Gu, Lin Qi, Guo-Jun Li, xiaoxiao Zhang, Renrui Zhang, Zenghui Harada, Tatsuya Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Texas Arlington Arlington TX 76019 USA RIKEN AIP Chuo City Japan Univ Tokyo Tokyo Japan Lab Machine Percept & Learning Orlanda FL USA Univ British Columbia Vancouver BC Canada Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9783031200762;9783031200779

Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images. Most of these algorithms assume the degradation is fixed and known a priori. However, in practical, either the real degradation or optimal up-sampling ratio rate is unknown or differs from assumption, leading to a deteriorating performance for both the pre-processing module and the consequent high-level task such as object detection. Here, we propose a novel self-supervised framework to detect objects in degraded low resolution images. We utilizes the downsampling degradation as a kind of transformation for self-supervised signals to explore the equivariant representation against various resolutions and other degradation conditions. The Auto Encoding Resolution in Self-supervision (AERIS) framework could further take the advantage of advanced SR architectures with an arbitrary resolution restoring decoder to reconstruct the original correspondence from the degraded input image. Both the representation learning and object detection are optimized jointly in an end-to-end training fashion. The generic AERIS framework could be implemented on various mainstream object detection architectures with different backbones. The extensive experiments show that our methods has achieved superior performance compared with existing methods when facing variant degradation situations. Code is available at this link https://github. com/cuiziteng/ECCV AERIS.

关键词： Self-supervised learning Computational photography Object detection

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Reconfigurable video tracker

Reconfigurable video tracker

引用

Acquisition, Tracking, and Pointing xIII conference

作者： Groves, GK White, SW Vahey, MD Harding, JA Raytheon Syst Co El Segundo CA 90245 USA

ISBN: (纸本)0819431664

Reconfigurable computing using SRAM-based field programmable gate arrays (FPGAs) can achieve significant (10x-100x) computational performance advantages over conventional programmable processors. Since FPGAs can be customized, reconfigurable computers can provide optimal logic-circuitry for distinct phases of an application resulting in superior performance compared to generic multi-purpose hardware implementations. This performance improvement can be accomplished by reallocating logic resources to address the critical task at-hand. Consequently, not only can reconfigurable processors provide higher performance than programmable processors;they also enable common module architectures useful for multiple applications or programs. In this paper, we will describe a fielded, ruggedized, fully programmable, single card, image-based tracking system using a reconfigurable computing module. The reconfigurable computing board contains multiple FPGAs, which can be customized at-request by loading configuration data from the host processor to the module over the Peripheral Component Interface (PCI) bus. Configurations can be selectively loaded to a specific FPGA or multiple configurations can be loaded simultaneously to different devices. This system provides multiple video tracking algorithms, automatic and manual target acquisition, RS-170 video input/output, and command/data I/O on a single 6U VME format card. While the initial application for this reconfigurable system was image-based target tracking, its hardware reconfigurability allows it to be applied to a wide variety of image and signal processing applications, such as automatic target recognition, infrared search and track, and image enhancement.

关键词： reconfigurable computing image-based target tracking image processing

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