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Background modeling and subtraction is a core component of many vision based systems. By far the most popular background models are per-pixel models, in which each pixel is considered independently. Such models fail to handle dynamic backgrounds and noise. In this paper, we present a solution to this problem by proposing a novel and computationally simple spatio-temporal background model. We extend...
A threshold-based procedure to estimate sparse channels in an orthogonal frequency division multiplexing (OFDM) system is proposed. An optimal threshold is derived by maximising the probability of correct detection between significant and zero-valued taps estimated by the least squares (LS) estimator. Improved LS estimates are obtained by pruning the LS estimates with the statistically derived threshold.
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