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Signal Processing Letters, IEEE

Issue 7 • Date July 2000

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Displaying Results 1 - 10 of 10
  • Cut detection in video sequences using phase correlation

    Page(s): 173 - 175
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (62 KB)  

    A novel algorithm for the detection of cuts in video sequences is proposed. The algorithm uses phase correlation to obtain a measure of content similarity for temporally adjacent frames and responds very well to scene cuts. The algorithm is insensitive to the presence of global illumination changes and noise and outperforms established methods for cut detection. As the proposed scheme is implemented in the frequency domain, the availability of fast hardware makes the scheme attractive for interactive and on-line applications. View full abstract»

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  • A genetic algorithm for adaptive tomography of elliptical objects

    Page(s): 176 - 178
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (63 KB)  

    A probabilistic algorithm for on-line tomographic reconstruction of ellipse-like images is presented. The algorithm takes advantage of the characteristic preferential direction of the objects, constructing a guidance function to select the angles for subsequent radiographic projections. The simulation results confirm that the technique reduces the number of projections required to achieve a given quality limit. View full abstract»

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  • Optimal filtering for patterned displays

    Page(s): 179 - 181
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (43 KB)  

    Displays with repeating patterns of colored subpixels gain spatial resolution by setting individual subpixels rather than by setting entire pixels. This paper describes optimal filtering that produces subpixel values from a high-resolution input image. The optimal filtering is based on an error metric inspired by psychophysical experiments. Minimizing the error metric yields a linear system of equations, which can be expressed as a set of filters. These filters provide the same quality of font display as standard anti-aliasing at a point size 25% smaller. This optimization forms the filter design framework for Microsoft's ClearType. View full abstract»

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  • High resolution speech feature parametrization for monophone-based stressed speech recognition

    Page(s): 182 - 185
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (76 KB)  

    This letter investigates the impact of stress on monophone speech recognition accuracy and proposes a new set of acoustic parameters based on high resolution wavelet analysis. The two parameter schemes are entitled wavelet packet parameters (WPP) and subband-based cepstral parameters (SBC). The performance of these features is compared to traditional Mel-frequency cepstral coefficients (MFCC) for stressed speech monophone recognition. The stressed speaking styles considered are neutral, angry, loud, and Lombard effect speech from the SUSAS database. An overall monophone recognition improvement of 20.4% and 17.2% is achieved for loud and angry stressed speech, with a corresponding increase in the neutral monophone rate of 9.9% over MFCC parameters. View full abstract»

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  • Classes of smoothed Weyl symbols

    Page(s): 186 - 188
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (90 KB)  

    We propose a new class of time frequency (TF) symbols covariant to time shifts and frequency shifts on a random process. The new TF symbols are useful for analyzing linear time-varying systems or nonstationary random processes, and they are defined as TF-smoothed versions of the narrowband Weyl symbol. We derive kernel constraints for the new TF symbols to satisfy the unitarity property and the quadratic form. We also propose a new class of TF symbols covariant to time shifts and scale changes on a random process. These new TF symbols can be interpreted as affine-smoothed versions of the narrowband Weyl symbol or of the wideband P/sub 0/-Weyl symbol. View full abstract»

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  • The use of sample selection probabilities for stack filter design

    Page(s): 189 - 192
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (69 KB)  

    We propose a procedure for stack filter design that takes into consideration the filter's sample selection probabilities. A statistical optimization of stack filters can result in a class of stack filters, all of which are statistically equivalent. Such a situation arises in cases of nonsymmetric noise distributions or in the presence of constraints. Among the set of equivalent stack filters, our method constructs a statistically optimal stack filter whose sample selection probabilities are concentrated in the center of its window. This leads to improvement of detail preservation. View full abstract»

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  • Higher-order time frequency-based blind source separation technique

    Page(s): 193 - 196
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (77 KB)  

    This letter considers the separation and estimation of independent sources from their instantaneous linear mixed observed data. Here, unknown source signals are estimated from their unknown linear mixtures using the strong assumption that the sources are mutually independent. In practice, separation can be achieved by using suitable second- or higher-order statistics. The authors propose a novel source separation technique exploiting fourth-order time frequency distributions. A computationally feasible implementation is presented based on joint diagonalization of the matrices of the principal slices of time-multifrequency domain of support of the cumulant-based Wigner trispectrums. A numerical example demonstrates the effectiveness of the proposed approach. View full abstract»

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  • Blind separation of Gaussian sources via second-order statistics with asymptotically optimal weighting

    Page(s): 197 - 200
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (99 KB)  

    Blind separation of Gaussian sources with different spectra can be attained using second-order statistics. The second-order blind identification (SOBI) algorithm, proposed by Belouchrani et al. (1997), uses approximate joint diagonalization. We show that substantial improvement over SOBI can be attained when the joint diagonalization is transformed into a properly weighted nonlinear least squares problem. We provide an iterative solution and derive the optimal weights for our weights-adjusted SOBI (WASOBI) algorithm. The improvement is demonstrated by analysis and simulations. View full abstract»

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  • Estimation of the parameters of autoregressive signals from colored noise-corrupted measurements

    Page(s): 201 - 204
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (84 KB)  

    This paper is concerned with identification of autoregressive (AR) model parameters using observations corrupted with colored noise. A novel formulation of an auxiliary least-squares estimator is introduced so that the autocovariance functions of the colored observation noise can be estimated in a straightforward manner. With this, the colored-noise-induced estimation bias can be removed to yield the unbiased estimate of the AR parameters. The performance of the proposed unbiased estimation algorithm is illustrated by simulation results. The presented work greatly extends the author's previous methods that were developed for identification of AR signals observed in white noise. View full abstract»

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  • Nonlinear test statistic to improve signal detection in non-Gaussian noise

    Page(s): 205 - 207
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (85 KB)  

    We compare two simple test statistics that a detector can compute from multiple noisy data in a binary decision problem based on a maximum a posteriori probability (MAP) criterion. One of these statistics is the standard sample mean of the data (linear detector), which allows one to minimize the probability of detection error when the noise is Gaussian. The other statistic is even simpler and consists of a sample mean of a two-state quantized version of the data (nonlinear detector). Although simpler to compute, we show that this nonlinear detector can achieve smaller probability of error compared to the linear detector. This especially occurs for non-Gaussian noises with heavy tails or a leptokurtic character. View full abstract»

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Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

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Meet Our Editors

Editor-in-Chief
Peter Willett
University of Connecticut
Storrs, CT 06269
peter.willett@uconn.edu