The aim of our work is to implement a system of automatic face image processing on DSP's : face detection in an image, face recognition and face identification. The first step is to localize the face in an image. ...
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ISBN:
(纸本)0819432938
The aim of our work is to implement a system of automatic face image processing on DSP's : face detection in an image, face recognition and face identification. The first step is to localize the face in an image. Our approach consists to approximate the face oval shape with an ellipse and to compute coordinates of the center of the ellipse. For this purpose, we explore a new version of the Hough transformation : the Fuzzy Generalized Hough transformation. To reduce the computation time, we present also several parallel implementations of the algorithm on a multi-DSP architecture using SynDEx tool which is a programming environment to generate optimized distributed real-time executives. We show that an acceleration of factor 1.7 has been obtained.
It is well known that high-dimensional integrals can be solved with Monte Carlo algorithms. Recently, it was discovered that there is a relationship between low discrepancy sets and the efficient evaluation of higher-...
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ISBN:
(纸本)0819432938
It is well known that high-dimensional integrals can be solved with Monte Carlo algorithms. Recently, it was discovered that there is a relationship between low discrepancy sets and the efficient evaluation of higher-dimensional integrals. Theory suggests that for midsize dimensional problems, algorithms based on low discrepancy sets should out perform all other existing methods by an order of magnitude in terms of the number of sample points used to evaluate the integrals. We show that the field of image processing can potentially take advantage of specific properties of low discrepancy sets. To illustrate this, we applied the theory of low discrepancy sequences to some relatively simple image processing and computer vision related operations such as the estimation of gray level image statistics, fast location of objects in a binary image and the reconstruction of images from a sparse set of points. Our experiments show that compared to standard methods, the proposed new algorithms are faster and statistically more robust. Classical low discrepancy sets based on the Halton and Sobol' sequences were investigated thoroughly and showed promising results. The use of low discrepancy sequences in image processing for image characterization, understanding and object recognition is a novel and promising area for further investigation.
This paper presents novel methods of designing analog Cellular Nonlinear (Neural) Networks (CNNs) to implement very low-noise binary addition. In these techniques the continuous characteristic of the current that char...
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ISBN:
(纸本)0819432938
This paper presents novel methods of designing analog Cellular Nonlinear (Neural) Networks (CNNs) to implement very low-noise binary addition. In these techniques the continuous characteristic of the current that charges (discharges) the load capacitor leads to a virtually switching free addition process that significantly reduces the switching noise. This switching mechanism also leads to higher slew of output voltage during the transitions which in turn reduces the cross talk. Simulation results demonstrate a three orders of magnitude reduction in the noise generated by this structure compared to that generated by a digital adder running at the same speed. This very good noise performance of these new adder structures makes them suitable choices for low to moderate speed high precision mixed signal applications.
We generalize the concept of the autocorrelation function to arbitrary physical variables and show how it can be used to define a local autocorrelation function. Using the local autocorrelation function we develop a n...
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ISBN:
(纸本)0819429163
We generalize the concept of the autocorrelation function to arbitrary physical variables and show how it can be used to define a local autocorrelation function. Using the local autocorrelation function we develop a new method to generate densities for arbitrary physical quantities. In addition, we show that the generalized autocorrelation function can be used to characterize functions with respect to a physical property.
Searching for wideband short duration chirps is an important issue in spectrum surveillance. We propose a method and apparatus, inspired by optical tomography, by which a one-dimensional signal is converted to a two-d...
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ISBN:
(纸本)0819429163
Searching for wideband short duration chirps is an important issue in spectrum surveillance. We propose a method and apparatus, inspired by optical tomography, by which a one-dimensional signal is converted to a two-dimensional image. This image has the remarkable property that it may disclose discernible structure. A chirp in additive white Gaussian noise, even undersampled, may be detected. The process is inherently linear and may be easily implemented by parallel processing or through the construction of an optoelectronic device.
This paper presents an application of formal mathematics to create a high performance, low power architecture for time-frequency and time-scale computations implemented in asynchronous circuit technology that achieves...
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ISBN:
(纸本)0819429163
This paper presents an application of formal mathematics to create a high performance, low power architecture for time-frequency and time-scale computations implemented in asynchronous circuit technology that achieves significant power reductions and performance enhancements over more traditional approaches. Utilizing a combination of concepts from multirate signalprocessing and asynchronous circuit design, a case study is presented dealing with a new architecture for the fast Fourier transform, an algorithm that requires globally shared results. Then, the generalized distributive law is presented as an important paradigm for advanced asynchronous hardware design.
A deconvolution technique to estimate the Evolutionary Spectrum (ES) of nonstationary signals by deconvolving the blurring effects of the kernel function fi om bilinear time frequency distributions (TFD) is presented....
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ISBN:
(纸本)0819429163
A deconvolution technique to estimate the Evolutionary Spectrum (ES) of nonstationary signals by deconvolving the blurring effects of the kernel function fi om bilinear time frequency distributions (TFD) is presented. The resulting ES has desirable properties such as positivity, higher resolution, higher concentration in time-frequency. The proposed algorithm is computationally more efficient compared to the recently proposed entropy based deconvolution method. Unlike the entropy method the new algorithm can be adapted to deconvolve TFDs other than the spectrogram.
In this report, we propose combining the Total Variation denoising method with the high loss wavelet compression for high noise level images. Numerical experiments show that TV-denoising can bring more wavelet coeffic...
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ISBN:
(纸本)0819429163
In this report, we propose combining the Total Variation denoising method with the high loss wavelet compression for high noise level images. Numerical experiments show that TV-denoising can bring more wavelet coefficients closer to zero thereby making the compression more efficient while the salient features (edges) of the images can still be retained.
The proceedings contains 58 papers from the conference of SPIE: advancedsignalprocessingalgorithms, architectures, and implementations VIII. The topics discussed include: blind channel identification and extraction...
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The proceedings contains 58 papers from the conference of SPIE: advancedsignalprocessingalgorithms, architectures, and implementations VIII. The topics discussed include: blind channel identification and extraction of more sources than sensors;blind channel estimation for CDMA systems with orthogonal modulation;blind equalization and source separation with MSK inputs and adaptive blind channel estimation by least-squares smoothing for CDMA.
We briefly review the signalprocessing architecture of a wireless MEM sensor system for source detection, signal enhancement, localization, and identification. A blind beamformer using only the measured data of rando...
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ISBN:
(纸本)0819429163
We briefly review the signalprocessing architecture of a wireless MEM sensor system for source detection, signal enhancement, localization, and identification. A blind beamformer using only the measured data of randomly distributed sensors to form a sample correlation matrix is proposed. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector of the sample correlation matrix. An effective blind beamforming estimation of the time delays of the dominant source is demonstrated. Source localization based on a novel least-squares method for time delay estimation is also given. Array system performance based on analysis, simulation, and measured acoustical/seismic sensor data is presented. Applications of such a system to multimedia, intrusion detection, and surveillance are briefly discussed.
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