To a large extent noise suppression algorithms have been designed to deal with the two most classically defined types of noise: impulsive and Gaussian noise. However digitized imag...
Object tracking algorithms extensively found in literature are either constrained with assumptions or are overly sensitive to noise. We propose and successfully test two new weigh...
We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a "null category noise model" (NCN...
In this paper a exible, high-throughput, low-complexity additive white gaussian noise (AWGN) channel generator is presented. The proposed generator employs a Mersenne-Twister to g...
The paper proposes a modification of the standard maximum a posteriori (MAP) method for the estimation of the parameters of a Gaussian process for cases where the process is supe...