The spectral reflectance of a biological tissue is known to be affected by its physical and optical properties such as thickness, chromophore concentrations and scattering coefficient. There exist numerous methods that aim to extract the optical parameters of a tissue by relating reflectance measurements to a theoretical model of light transport. During the parameter recovery process, assumptio...

Using first principles, we establish in this paper a connection between the maximum a posteriori (MAP) estimator and the variational formulation of optimizing a given functional subject to some noise constraints. A MAP estimator which uses a Markov or a maximum entropy random field model for a prior distribution can be viewed as a minimizer of a variational problem. Using notions from robust st...

The maximum amplitude algorithm (MAA) is generally utilized in the estimation of the pressure values, and it uses heuristically obtained ratios of systolic and diastolic oscillometric amplitude to the mean arterial pressure (known as systolic and diastolic ratios) in order to estimate the systolic and diastolic pressures. This paper proposes a Bayesian model to estimate the systolic and diastol...

High dynamic range (HDR) image synthesis from multiple low dynamic range exposures continues to be actively researched. The extension to HDR video synthesis is a topic of significant current interest due to potential cost benefits. For HDR video, a stiff practical challenge presents itself in the form of accurate correspondence estimation of objects between video frames. In particular, loss of ...

Sequential Monte Carlo (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of the a posteriori probability measures generated by state-space dynamical models. At any given time t, a SMC method produces a set of samples over the state space of the system of interest (often termed “particles”) that is used to build a discrete and random a...

We propose to use a new feature transformation (FT) function to construct supervectors of support vector machines for speaker recognition. Considering that estimation of bias vectors is more robust than that of transformation matrices, we define the FT function in a flexible form that transformation matrices and bias vectors are controlled by separate regression classes. Unlike the MLLR-based a...