Results 1  10
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471
A computational approach to edge detection
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1986
"... This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumpti ..."
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Cited by 4675 (0 self)
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with different signaltonoise ratios in the image. We present a general method, called feature synthesis, for the finetocoarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function
Independent Factor Analysis
 Neural Computation
, 1999
"... We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and unifies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square no ..."
Abstract

Cited by 277 (9 self)
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noiseless mixing, but also the general case where the number of mixtures differs from the number of sources and the data are noisy. IFA is a twostep procedure. In the first step, the source densities, mixing matrix and noise covariance are estimated from the observed data by maximum likelihood
Maximum Likelihood Estimator for Jitter Noise Models
"... Abstract—Highfrequency sampling scopes suffer from both additive noise and time jitter. The classical techniques for identifying the additive noise and time jitter noise are based on linear least squares (LS) estimators. This work derives a maximum likelihood (ML) estimator and compares its perfor ..."
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Abstract—Highfrequency sampling scopes suffer from both additive noise and time jitter. The classical techniques for identifying the additive noise and time jitter noise are based on linear least squares (LS) estimators. This work derives a maximum likelihood (ML) estimator and compares its
Maximum likelihood estimation of signal amplitude and noise variance from MR data. Magnetic Resonance in Medicine
, 2004
"... In magnetic resonance imaging, the raw data, which are acquired in spatial frequency space, are intrinsically complex valued and corrupted by Gaussian distributed noise. After applying an inverse Fourier transform the data remain complex valued and Gaussian distributed. If the signal amplitude is t ..."
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Cited by 22 (0 self)
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. Similarly, the noise variance can be estimated from both the complex and magnitude data sets. This paper addresses the question whether it is better to use complex valued data or magnitude data for the estimation of these parameters using the Maximum Likelihood method. As a performance criterion, the mean
Maximum likelihood estimation of the parameters of multiple sinusoids from noisy measurements
 IEEE Trans. Acoust., Speech, Signal Processing
, 1989
"... AbstractThe problem of estimating the frequencies, phases, and amplitudes of sinusoidal signals is considered. A simplified maximumlikelihood GaussNewton algorithm which provides asymptotically efficient estimates of these parameters is proposed. Initial estimates for this algorithm are obtained ..."
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Cited by 55 (5 self)
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AbstractThe problem of estimating the frequencies, phases, and amplitudes of sinusoidal signals is considered. A simplified maximumlikelihood GaussNewton algorithm which provides asymptotically efficient estimates of these parameters is proposed. Initial estimates for this algorithm
The psychometric function: I. Fitting, sampling, and goodness of fit
, 2001
"... The psychometric function relates an observer’s performance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. This paper, together with its companion paper (Wichmann & Hill, 2001), describes an integrated approach to (1) fitting psychometric funct ..."
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Cited by 219 (11 self)
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functions, (2) assessing the goodness of fit, and (3) providing confidence intervals for the function’s parameters and other estimates derived from them, for the purposes of hypothesis testing. The present paper deals with the first two topics, describing a constrained maximumlikelihood method of parameter
An Approximate Maximum Likelihood Single Tone Frequency Estimator
, 1998
"... The problem of estimating the frequency of a single tone buried in white Gaussian noise is studied. Starting from a necessary condition on the maximum likelihood estimator (MLE), a novel approximate MLE (AMLE) is derived, and an alternative derivation of Tretter's frequency estimator is outline ..."
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The problem of estimating the frequency of a single tone buried in white Gaussian noise is studied. Starting from a necessary condition on the maximum likelihood estimator (MLE), a novel approximate MLE (AMLE) is derived, and an alternative derivation of Tretter's frequency estimator
Maximum Likelihood Methods in Radar Array Signal Processing
, 1997
"... We consider robust and computationally efficient maximum likelihood algorithms for estimating the parameters of a radar target whose signal is observed by an array of sensors in interference with unknown second order spatial statistics. Two data models are described, one that uses the target directi ..."
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Cited by 41 (3 self)
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We consider robust and computationally efficient maximum likelihood algorithms for estimating the parameters of a radar target whose signal is observed by an array of sensors in interference with unknown second order spatial statistics. Two data models are described, one that uses the target
Performance analysis of maximumlikelihood semiblind estimation of MIMO channels
 in Proc. IEEE 63rd Vehic. Techn. Conf. (VTC
, 2006
"... Abstract — Iterative channel estimation and data detection is a very useful method to improve the channel estimation quality without sacrificing the bandwidth efficiency. Since both the known training symbols (nonblind) and the unknown data symbols (blind) are used for channel estimation, correspon ..."
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Cited by 14 (1 self)
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, corresponding techniques are referred to as semiblind. If the channel estimator and data detector are both optimal in the sense of maximumlikelihood criterion, we may call the algorithm as maximumlikelihood (ML) semiblind channel estimation (SBCE). This paper deals with MLSBCE for frequencyflat multi
Conditional Maximum Likelihood Timing Recovery: Estimators and Bounds
 IEEE Trans. on Signal Processing
, 2001
"... This paper is concerned with the derivation of new estimators and performance bounds for the problem of timing estimation of (linearly) digitally modulated signals. The conditional maximum likelihood (CML) method is adopted, in contrast to the classical lowSNR unconditional ML (UML) formulation tha ..."
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Cited by 15 (2 self)
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This paper is concerned with the derivation of new estimators and performance bounds for the problem of timing estimation of (linearly) digitally modulated signals. The conditional maximum likelihood (CML) method is adopted, in contrast to the classical lowSNR unconditional ML (UML) formulation
Results 1  10
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471