Convective regularization for optical flow

We argue that the time derivative in a fixed coordinate frame may not be the most appropriate measure of time regularity of an optical flow field. Instead, for a given velocity field $v$ we consider the convective acceleration $v_t + \nabla v v$ which describes the acceleration of objects moving according to $v$. Consequently we investigate the suitability of the nonconvex functional $\|v_t + \nabla v v\|^2_{L^2}$ as a regularization term for optical flow. We demonstrate that this term acts as both a spatial and a temporal regularizer and has an intrinsic edge-preserving property. We incorporate it into a contrast invariant and time-regularized variant of the Horn-Schunck functional, prove existence of minimizers and verify experimentally that it addresses some of the problems of basic quadratic models. For the minimization we use an iterative scheme that approximates the original nonlinear problem with a sequence of linear ones. We believe that the convective acceleration may be gainfully introduced in a variety of optical flow models

How did the Sovereign debt crisis affect the Euro financial integration? A fractional cointegration approach.

This paper examines financial integration among stock markets in the Eurozone using the prices from each stock index. Monthly time series are constructed for four major stock indices for the period between 1998 and 2016. A fractional cointegrated vector autoregressive model is estimated at an international level. Our results show that there is a perfect and complete Euro financial integration. Considering the possible existence of structural breaks, this paper also examines the fractional cointegration within each regime, showing that Euro financial integration is very robust. However, in the financial and sovereign debt crisis regime, IBEX 35 appears to be the weak link in Euro financial integration, unless Euro financial integration recovers when this period ends

Extremal points of total generalized variation balls in 1D:characterization and applications

The total generalized variation (TGV) is a popular regularizer in inverse problems and imaging combining discontinuous solutions and higher order smoothing. In particular, empirical observations suggest that its order two version strongly favors piecewise affine functions. In the present manuscript, we formalize this statement for the one-dimensional TGV-functional by characterizing the extremal points of its sublevel sets with respect to a suitable quotient space topology. These results imply that 1D TGV-regularized linear inverse problems with finite dimensional observations admit piecewise affine minimizers. As further applications of this characterization we include precise first-order necessary optimality conditions without requiring convexity of the fidelity term, and a simple solution algorithm for TGV-regularized minimization problems.Comment: 38 pages, 9 figure