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A Bayesian approach to tracking multiple targets using sensor arrays and particle filters
TLDR
A Bayesian approach to tracking the direction-of-arrival (DOA) of multiple moving targets using a passive sensor array using a collection of target states that can be viewed as samples from the posterior of interest.
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Detection of missing data in image sequences
TLDR
Heuristic and model-based methods for the detection of these missing data regions are presented in this paper, and their action on simulated and real sequences is compared.
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Bayesian curve fitting using MCMC with applications to signal segmentation
TLDR
Some Bayesian methods to address the problem of fitting a signal modeled by a sequence of piecewise constant linear regression models, for example, autoregressive or Volterra models are proposed.
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Interpolation of missing data in image sequences
TLDR
A number of model based interpolation schemes tailored to the problem of interpolating missing regions in image sequences, and comparisons with earlier work using multilevel median filters demonstrate the higher reconstruction fidelity of the new interpolators.
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Approximation of α-stable probability densities using finite Gaussian mixtures
TLDR
The new analytical model is based on a corollary of the mixing theorem for symmetric α-stable (SαS) random variables which states that a SαS r.v. can be expressed as the product of a Gaussian r.f. and a positive-stable r.d.s.
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Near optimal detection of signals in impulsive noise modeled with a symmetric /spl alpha/-stable distribution
TLDR
A novel near-optimal receiver for the detection of signals in symmetric /spl alpha/-stable noise is introduced which is very close to the locally optimum receiver and is significantly better than the performance of previously suggested sub-optimum receivers.
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Markov chain Monte Carlo methods with applications to signal processing
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Content based image retrieval through object extraction and querying
TLDR
It is shown that the ability to access images at the level of objects is essential for CBIR, and the superiority of the approach over the global histogram approach is shown.
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New Geometric Methods for Computer Vision: An Application to Structure and Motion Estimation
TLDR
A coordinate-free approach to the geometry of computer vision problems is discussed, believing the present formulation to be the only one in which least-squares estimates of the motion and structure are derived simultaneously using analytic derivatives.
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Least Lp-norm impulsive noise cancellation with polynomial filters
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