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Recently, sparse representation based classification has been widely used in pattern recognition. Most of existing methods exploit the recovered representation coefficients to reconstruct the inputs, and the classwise reconstruction errors are used to identify the class of the sample based on the subspace assumption. Different from the reconstruction pipeline, an assignment framework is built on the representation coefficients in this paper.

Multiple scattering effects are commonly ignored in the detection and estimation of scatterers in signal processing research, because the energy of the first-order scattering is much larger than that of higher-order components. Although multiple scattering can significantly increase the estimation precision of point scatterers, it does not always lead to an improvement. Identifying conditions under which multiple scattering is beneficial or detrimental to estimation in a general setup is still an open problem.

This is an overview poster of the paper INFORMATION POINT SET REGISTRATION FOR SHAPE RECOGNITION.

In many estimation problems of interest the unknown parameters reside on spherical manifolds. As most common filtering algorithms assume that parameters have Gaussian prior distributions, their application to such problems leads to suboptimal performance. In this letter, we propose a model in which the unknown unit-norm parameter vectors have Fisher-Bingham (F-B) prior distributions.

We introduce a novel, transient model for the electroencephalogram (EEG) as the noisy addition of linear filters responding to trains of delta functions. We set the synthesis part as a parameter-tuning problem and obtain synthetic EEG-like data that visually resembles brain activity in the time and frequency domains. For the analysis counterpart, we use sparse approximation to decompose the signal in relevant events via Matching Pursuit.

The growing need for a powerful scalable video coding engine targeting the heterogeneous landscape of network, devices, and consumption environments has led to the development of the Scalable High Efficiency Video Coding (SHVC) standard, an extension of the High Efficiency Video Coding (HEVC) standard. To improve the SHVC compression efficiency, this paper proposes a novel joint layer coding mode to be integrated in the SHVC codec.

The mean curvature has been shown a proper regularization in various ill-posed inverse problems in signal processing. Traditional solvers are based on either gradient descent methods or Euler Lagrange Equation. However, it is not clear if this mean curvature regularization term itself is convex or not. In this paper, we first prove that the mean curvature regularization is convex if the dimension of imaging domain is not