We present a novel method for estimation of articulated motion in depth scans. The method is based on a framework for regularization of vectorand matrixvalued functions on parametric surfaces. We extend augmented-Lagrangian total variation regularization to smooth rigid motion cues on the scanned 3D surface obtained from a range scanner. We demonstrate the resulting smoothed motion maps to be a...

The present paper provides a systematic way to generalize Takagi-Sugeno observer design for discrete-time nonlinear descriptor models. The approach is based on Finsler’s lemma, which decouples the observer gains from the Lyapunov function. The results are expressed as strict LMI constraints. To obtain more degrees of freedom without altering the number of LMI constraints and thus relax the cond...

Let us consider a case where all of the elements in some continuous slices are missing in tensor data. In this case, the nuclear-norm and total variation regularization methods usually fail to recover the missing elements. The key problem is capturing some delay/shift-invariant structure. In this study, we consider a low-rank model in an embedded space of a tensor. For this purpose, we extend a...

In this paper we derive a generalizing concept of G-norms, which we call G-sets, which is used to characterize minimizers of non-differentiable regularization functionals. Moreover, the concept is closely related to the definition of slopes as published in a recent book by Ambrosio, Gigli, Savaré. A paradigm of regularization models fitting in this framework is robust bounded variation regulari...

Clinical diagnosis environments often require the availability of processed data in real-time, unfortunately, reconstruction times are prohibitive on conventional computers, neither the adoption of expensive parallel computers seems to be a viable solution. Here, we focus on development of mathematical software on high performance architectures for Total Variation based regularization reconstru...