The Kalman filter is a well-established approach to get information on the time-dependent state of a system from noisy observations. It was developed in the context of the Apollo project to see the deviation of the true trajectory of a rocket from the desired trajectory. Afterwards it was applied to many different systems with small numbers of components of the respective state vector (typically about 10). In all cases the equation of motion for the state vector was known exactly. The fast dissipative magnetization dynamics is often investigated by x-ray magnetic circular dichroism movies (XMCD movies), which are often very noisy. In this situation the number of components of the state vector is extremely large (about 105), and the equation of motion for the dissipative magnetization dynamics (especially the values of the material parameters of this equation) is not well known. In the present paper it is shown by theoretical considerations that – nevertheless – there is no principle problem for the use of the Kalman filter to denoise XMCD movies of fast dissipative magnetization dynamics.
Latest climate projections suggest that both frequency and intensity of climate extremes will be substantially modified over the course of the coming decades. As a consequence, we need to understand to what extent and via which pathways climate extremes affect the state and functionality of terrestrial ecosystems and the associated biogeochemical cycles on a global scale. So far the impacts of climate extremes on the terrestrial biosphere were mainly investigated on the basis of case studies, while global assessments are widely lacking. In order to facilitate global analysis of this kind, we present a methodological framework that firstly detects spatiotemporally contiguous extremes in Earth observations, and secondly infers the likely pathway of the preceding climate anomaly. The approach does not require long time series, is computationally fast, and easily applicable to a variety of data sets with different spatial and temporal resolutions. The key element of our analysis strategy is to directly search in the relevant observations for spatiotemporally connected components exceeding a certain percentile threshold. We also put an emphasis on characterization of extreme event distribution, and scrutinize the attribution issue. We exemplify the analysis strategy by exploring the fraction of absorbed photosynthetically active radiation (fAPAR) from 1982 to 2011. Our results suggest that the hot spots of extremes in fAPAR lie in Northeastern Brazil, Southeastern Australia, Kenya and Tanzania. Moreover, we demonstrate that the size distribution of extremes follow a distinct power law. The attribution framework reveals that extremes in fAPAR are primarily driven by phases of water scarcity.
In pages: 2392 - 2399, 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2012 (Conference Paper)
Image denoising can be described as the problem of
mapping from a noisy image to a noise-free image. The
best currently available denoising methods approximate
this mapping with cleverly engineered algorithms. In this
work we attempt to learn this mapping directly with a plain
multi layer perceptron (MLP) applied to image patches.
While this has been done before, we will show that by training on large image databases we are able to compete with the current state-of-the-art image denoising methods. Furthermore, our approach is easily adapted to less extensively studied types of noise (by merely exchanging the training data), for which we achieve excellent results as well.
In Computer Vision - ECCV 2012, LNCS Vol. 7574, pages: 187-200, (Editors: A Fitzgibbon, S Lazebnik, P Perona, Y Sato, and C Schmid), Springer, Berlin, Germany, 12th IEEE European Conference on Computer Vision, ECCV, 2012 (Conference Paper)
Camera lenses are a critical component of optical imaging systems, and lens imperfections compromise image quality. While traditionally, sophisticated lens design and quality control aim at limiting optical aberrations, recent works [1,2,3] promote the correction of optical flaws by computational means. These approaches rely on elaborate measurement procedures to characterize an optical system, and perform image correction by non-blind deconvolution.
In this paper, we present a method that utilizes physically plausible assumptions to estimate non-stationary lens aberrations blindly, and thus can correct images without knowledge of specifics of camera and lens. The blur estimation features a novel preconditioning step that enables fast deconvolution. We obtain results that are competitive with state-of-the-art non-blind approaches.
In Procedia Computer Science Volume 9, pages: 897-906, (Editors: H. Ali, Y. Shi, D. Khazanchi, M. Lees, G.D. van Albada, J. Dongarra, P.M.A. Sloot, J. Dongarra), Elsevier, Amsterdam, Netherlands, International Conference on Computational Science (ICCS), June 2012 (Conference Paper)
Classifying the land surface according to dierent climate zones is often a prerequisite for global diagnostic or
predictive modelling studies. Classical classifications such as the prominent K¨oppen–Geiger (KG) approach rely on
heuristic decision rules. Although these heuristics may transport some process understanding, such a discretization
may appear “arbitrary” from a data oriented perspective. In this contribution we compare the precision of a KG
classification to an unsupervised classification (k-means clustering). Generally speaking, we revisit the problem of
“climate classification” by investigating the inherent patterns in multiple data streams in a purely data driven way. One question is whether we can reproduce the KG boundaries by exploring dierent combinations of climate and remotely sensed vegetation variables. In this context we also investigate whether climate and vegetation variables build similar clusters. In terms of statistical performances, k-means clearly outperforms classical climate classifications. However, a subsequent stability analysis only reveals a meaningful number of clusters if both climate and vegetation data are considered in the analysis. This is a setback for the hope to explain vegetation by means of climate alone. Clearly, classification schemes like K¨oppen-Geiger will play an important role in the future. However, future developments in this area need to be assessed based on data driven approaches.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems