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Fixed structure systolic array and processing of images

Fixed structure systolic array and processing of images

引用

9th Mediterranean Electrotechnical conference (Melecon 98)

作者： Zajc, M Sernec, R Tasic, JF Univ Ljubljana Fac Elect Engn Ljubljana 1000 Slovenia

ISBN: (纸本)0780338790

The paper presents a fixed structure systolic array for perform a set of computationally and real-time demanding problems that frequently arise in the area of image processing. The fixed structure systolic array implements Faddeev Algorithm, which could be interpreted as generalised Gauss elimination. Modification of the algorithm is considered for improved stability and accuracy. The computations of the modified algorithm are presented in the form of SFG. This is followed with possible applications and additional extensions of the proposed systolic array structure. Enormous computational and real-time requirements for signal and image processing problems support development of fast application specific structures capable of improving the performance for several orders of magnitude compared to general-purpose computer architectures [1]. Parallel computing is gaining increasing importance with the evolution of algorithms for image and signal processing, advances in VLSI technology and ever-broader range of applications. In high-end computing various parallel structures from powerful supercomputers to processor arrays are employed to meet the demands of real-time problems. From a sequential computation point of view, a fast algorithm is the one using a reduced number of operations, which is an important measure of speed in sequential contest. Many DSP algorithms can serve as an example, DFT algorithm compared to several versions of FFT is probably the most evident. In parallel processing the level of concurrency can become more important than the actual number of operations. Ability to parallelise an algorithm and the proportion of sequential code can severely impact the performance of the parallel structure.

关键词： image processing

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Continuous speech segmentation determined by blind source separation 5

Continuous speech segmentation determined by blind source se...

引用

SPIE conference on Wavelet applications V

作者： Szu, H Hsu, C xie, DH Univ SW Louisiana Ctr Adv Comp Studies Lafayette LA 70504 USA

ISBN: (纸本)081942840X

One of the problems of 5% error rate encountered in Continuous speech recognition is partly due to the difficulty in the identification of a mixed up of two phonemes in a close concatenation. For instance, one speaks of "Let's go" instead of "Let us go". There are two kinds of speech segmentations: the linguistic segmentation and the acoustic segmentation. The linguistic segmentation relies on a combination of acoustic, lexical, semantic, and statistical knowledge sources, which has been studied in [1,2,3,4]. Daily spoken conversations are usually abbreviated for speakers' convenience. The acoustic segmentation is to separate the mixed sounds such as /ts/ into /t/ & /s/ for automatically finding linguistic units. Adaptive Wavelet Transform (AWT) developed by Szu [6,9] is a linear superposition of banks of constant-Q zero-mean mother wavelets implemented by an Artificial Neural Network (ANN) called a "WaveNet". Each neuron is represented by a daughter wavelet, which can be an affine scale change of identical or different mother wavelet for a continuous AWT. AWT was designed for the cocktail party effect and to solve the acoustic segmentation of phonemes using a supervised learning ANN architecture. In this paper, we reviewed AWT from Independent Component Analysis (ICA) viewpoint, and then applied Blind Source Separation (BSS) to the acoustic de-mixing and segmentation.

关键词： wavelets Acoustics Independent component analysis Superposition Neural networks Neurons Feature extraction signal processing Wavelet transforms Speech recognition

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A hardware and software platform for analog sensor processing using exposure control

A hardware and software platform for analog sensor processin...

引用

EUROPTO conference on Advanced Focal Plane Arrays and Electronic Cameras II

作者： Gustafsson, A Åström, A Linkoping Univ Dept Elect Engn Imaging Proc Lab S-58183 Linkoping Sweden

ISBN: (纸本)0819428620

Analog Sensor processing Using Exposure Control (ASPEC) is a new concept for high speed image processing. By using an addressable image array with integrating output amplifiers, signal processing can be performed directly on the sensor. The major gain in using ASPEC techniques is that the operations are fast, the approach can be implemented using existing hardware, and that the processing is executed in parallel on the sensor array. Furthermore, the data reduction is carried out early in the signal processing chain. In this paper we present a novel programmable camera architecture based on the CMS CMOS integrating addressable image sensor which is well suited for ASPEC applications.

关键词： Analog Sensor processing Using Exposure Control ASPEC computer architecture sheet-of-light

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Proceedings of SPIE: Configurable Computing: Technology and applications

Proceedings of SPIE: Configurable Computing: Technology and ...

引用

Configuralble Computing: Technology and applications

This Volume 3526 of the conference proceedings contains 29 papers. Topics discussed include configurable computing technology and applications, image processing, applications development, architectures, digital signal... 详细信息

关键词： Parallel processing systems

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Edge-preserving MAP-estimation of motion vector fields in noisy low-dose x-ray image sequences

Edge-preserving MAP-estimation of motion vector fields in no...

引用

conference on applications of Digital image processing xxI

作者： Aach, T Univ Lubeck Inst Signal Proc & Proc Control D-23538 Luebeck Germany

ISBN: (纸本)0819429155

We describe a motion compensated temporally recursive noise reduction technique especially suited for sequences of moving x-ray images, where we focus on a robust motion estimator which is able to deal with the high noise levels in such images. These noise levels are caused by the very low x-ray dose rates used in medical real-time imaging (quantum-limited imaging). The robustness of our motion estimator is achieved by spatiotemporal regularization using a generalized Gauss-Markov random field. Unlike quadratic regularization by Gauss-Markov random fields, generalized Gauss-Markov random fields are able to account for motion edges without the need to explicitly specify an detection threshold. Instead, our model controls edges by a 'soft' parameter, which, gradually allows the regularization term to behave like a median filter, which preserves edges without using detection thresholds.

关键词： x-ray image restoration quantum noise Bayesian motion estimation generalized Gauss-Markov random fields thresholdless edge model

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Approximation by structured lower rank matrices

Approximation by structured lower rank matrices

引用

SPIE conference on Advanced signal processing Algorithms, Architectures, and Implementations VIII

作者： Chu, MT Funderlic, RE Plemmons, RJ N Carolina State Univ Dept Math Raleigh NC 27695 USA

ISBN: (纸本)0819429163

We consider two general procedures for constructing the nearest approximation of a given matrix by one with any lower rank and any linear structure. Nearness can be measured in any matrix norm. Structured low rank matrices arise in various applications, including image enhancement and model reduction. In practice, the empirical data collected in the matrix often either do not maintain the specified structure or do not induce the desirable rank. It is an important task to search for the nearest structured lower rank approximation of a given matrix. The techniques developed in this paper can easily be implemented for numerical computation. In particular, it is shown that the computations can be approached using efficient optimization packages. The special case of Toeplitz structure using the Frobenius matrix norm is discussed in detail to illustrate the ideas, and numerical tests are reported. The procedures developed herein can be generalized to consider a much broader range of approximation problems.

关键词： Matrices signal processing MATLAB Optimization (mathematics) image enhancement Computer programming Computer science Fourier transforms image processing Mathematics

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Speckle noise reduction for optical coherence tomography 3

Speckle noise reduction for optical coherence tomography

引用

Optical and Imaging Techniques for Biomonitoring III conference

作者： xiang, SH Zhou, L Schmitt, JM Hong Kong Univ Sci & Technol Dept Elect & Elect Engn Biomed Engn Lab Kowloon Hong Kong

ISBN: (纸本)0819426288

Optical coherence tomography (OCT) has shown great promise for non-invasive and contact-less imaging of subsurface soft tissues. However, the problem of low image contrast caused by speckle noise has limited its applications in diagnosis. This paper presents a wavelet-based method designed to improve OCT image contrast by reducing speckle noise. After transforming the image into a set of sub-images with different resolution levels in wavelet domain, we threshold high-frequency coefficients nonlinearly in horizontal, vertical, and diagonal directions. The experimental results show that wavelet processing suppresses speckle noise in OCT images of soft tissue effectively, while maintaining the sharpness of image features.

关键词： speckle optical coherence tomography wavelets

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Improving resolution of solid-state linear array x-Ray detectors

Improving resolution of solid-state linear array X-Ray detec...

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conference on Solid State Sensor Arrays - Development and applications II

作者： Borras, G Odet, C Vuorinen, K Gaffiot, F Jacquemod, G Inst Natl Sci Appl CREATIS F-69621 Villeurbanne France

ISBN: (纸本)0819427411

Linear solid-state detectors are nowadays a widespread media in industrial and medical x-ray imaging. The resolution reached with this systems has been largely improved in this past years, but is still too poor for some high resolution applications. We first have carried out an optimization of the detector characteristics through a behavioral simulation using a Hardware Description Language (HDLA(TM)). Furthermore, our work concerned the resolution enhancement for this kind of detectors via signal processing. Our approach takes into account the modeled point spread function (PSF) of the system. This modeled PSF is obtained with a new edge technique. The knowledge about the system response is used in a restoration scheme in order to improve the response of the detector to the high frequencies in the digital image. The restoration problem is an ill posed problem and uses an inverse Wiener filtering. Another intrinsic limitation of solid-state detectors is the spatial sampling step. In order to overcome this problem, we also tested the feasibility of a finer sampling of the acquired image, by interlacing several slightly shifted acquisitions of the same test object. The restoration applied to this finer sampled signal results in a resolution enhancement that is theoretically impossible to reach with a single detector acquisition. Some experimental results obtained on a variable bar-space pattern phantom are presented. This kind of phantom allows for a precise evaluation of the Modulation Transfer Function (MTF) on the acquired and processed images. The contribution of the image processing to the resolution enhancement can thus be quantified.

关键词： simulation behavioral CCD x-ray MTF image restoration

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Warping functions in speech

Warping functions in speech

引用

conference on Wavelet applications in signal and image processing VI

作者： Umesh, S Cohen, L Nelson, D Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)0819429139

We describe experiments that we have performed that address the issue of the relation between the same enunciations by different speakers. Our previous work indicated that frequencies are approximately scaled uniformly. In this paper we report results addressing possible corrections to uniform scaling. Our results show that the scaling is non uniform, that is the formant frequencies of different speakers scale differently at different frequencies. We discuss how this leads to the mathematical issue of separating the spectrum into a speaker dependent and speaker independent parts. We introduce the concept of a universal scaling function that is aimed at achieving this separation. The fundamental idea is to find a frequency axis transformation (warping function) which transforms the energy density spectrum (the squared absolute value of the Fourier transform of the enunciation) in such a way that a further Fourier transform of the resulting function achieves this separation. We discuss this procedure and relate it to the scale transform. Using real speech data we obtain such a transformation function. The resulting function is very similar to the Mel scale, which has been previously obtained only from psychoacoustic (hearing based) experiments. That similar scales are obtained from both hearing and speech production (as reported here) is fundamental to the understanding of speech and hearing.

关键词： Fermium Frequency modulation Transform theory Fourier transforms Receivers signal to noise ratio Ear Filtering (signal processing) signal analyzers Acoustics

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Wavelet based compression and denoising of convolution operators

Wavelet based compression and denoising of convolution opera...

引用

conference on Wavelet applications in signal and image processing VI

作者： Kompella, N Machiraju, R Choudhary, A Skjellum, A Mississippi State Univ NSF Engn Res Ctr Mississippi State MS 39762 USA

ISBN: (纸本)0819429139

In highly computationally intensive fields such as signal processing, image processing, computer graphics and visualization, much of the CPU time is spent in computing various transforms on the typically large data sets which may also contain noise. Though extensive work has been reported on the de-noising and subsequent compression of the data, little of it has been reported on the de-noising and subsequent compression of the operators. Moreover, in the work reported so far, thresholding, which is essential for achieving denoising, has not been based on a specific criterion. In the present work, we propose modifications to this approach which result in significant savings in the computational cost of the associated transformations. We present wavelet based approaches to compress different operators. We present two methods to accomplish this. In the first method, we first apply a non-standard wavelet transform on the operator represented in its matrix form. This step is followed by an adaptive thresholding scheme of Donohoe and Johnstone(3), which results in the de-noised form of the operator. In the second method, the original matrix representation of the operator is split into two sparse diagonal dominant matrices, one in the spatial domain and the other in the wavelet domain. Although, there is a need to use the original signal in the spatial domain, the resulting decomposition actually requires only a portion of the operators. More importantly, the decomposition results in representations with very little total error. We find that the computational complexity of the transformation, using these methods, reduces to O(N) as opposed to O(N-2) (where N is the size of the data vector) observed with using the original, denser representation of the operator. In particular, Method 2 allows even more expedient processing of signals with greater accuracy. Hence, many transformations with operators can be represented in a diagonally dominant matrix form resulting in signifi

关键词： signal processing operators thresholding denoising compression

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