GRASP News Volume 9, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory

A Temporally Coherent Neural Algorithm for Artistic Style Transfer

Within the fields of visual effects and animation, humans have historically spent countless painstaking hours mastering the skill of drawing frame-by-frame animations. One such animation technique that has been widely used in the animation and visual effects industry is called rotoscoping and has allowed uniquely stylized animations to capture the motion of real life action sequences, however it is a very complex and time consuming process. Automating this arduous technique would free animators from performing frame by frame stylization and allow them to concentrate on their own artistic contributions. This thesis introduces a new artificial system based on an existing neural style transfer method which creates artistically stylized animations that simultaneously reproduce both the motion of the original videos that they are derived from and the unique style of a given artistic work. This system utilizes a convolutional neural network framework to extract a hierarchy of image features used for generating images that appear visually similar to a given artistic style while at the same time faithfully preserving temporal content. The use of optical flow allows the combination of style and content to be integrated directly with the apparent motion over frames of a video to produce smooth and visually appealing transitions. The implementation described in this thesis demonstrates how biologically-inspired systems such as convolutional neural networks are rapidly approaching human-level behavior in tasks that were once thought impossible for computers. Such a complex task elucidates the current and future technical and artistic capabilities of such biologically-inspired neural systems as their horizons expand exponentially. Further, this research provides unique insights into the way that humans perceive and utilize temporal information in everyday tasks. A secondary implementation that is explored in this thesis seeks to improve existing convolutional neural networks using a biological approach to the way these models adapt to their inputs. This implementation shows how these pattern recognition systems can be greatly improved by integrating recent neuroscience research into already biologically inspired systems. Such a novel hybrid activation function model replicates recent findings in the field of neuroscience and shows significant advantages over existing static activation functions

Algorithms for the Analysis of Spatio-Temporal Data from Team Sports

Modern object tracking systems are able to simultaneously record trajectories—sequences of time-stamped location points—for large numbers of objects with high frequency and accuracy. The availability of trajectory datasets has resulted in a consequent demand for algorithms and tools to extract information from these data. In this thesis, we present several contributions intended to do this, and in particular, to extract information from trajectories tracking football (soccer) players during matches. Football player trajectories have particular properties that both facilitate and present challenges for the algorithmic approaches to information extraction. The key property that we look to exploit is that the movement of the players reveals information about their objectives through cooperative and adversarial coordinated behaviour, and this, in turn, reveals the tactics and strategies employed to achieve the objectives. While the approaches presented here naturally deal with the application-specific properties of football player trajectories, they also apply to other domains where objects are tracked, for example behavioural ecology, traffic and urban planning

18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems: Proceedings

Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.:Welcome Address ........................ Page I Table of Contents ........................ Page III Symposium Committees .............. Page IV Special Thanks ............................. Page V Conference program (incl. page numbers of papers) ................... Page VI Conference papers Invited talks ................................ Page 1 Regular Papers ........................... Page 14 Wednesday, May 26th, 2010 ......... Page 15 Thursday, May 27th, 2010 .......... Page 110 Friday, May 28th, 2010 ............... Page 210 Author index ............................... Page XII

Learning control policies from constrained motion

Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the task or the environment. Constraints are usually unobservable and frequently change between contexts. In this thesis, we explore the problem of learning control policies from data containing variable, dynamic and non-linear constraints on motion. We show that an effective approach for doing this is to learn the unconstrained policy in a way that is consistent with the constraints. We propose several novel algorithms for extracting these policies from movement data, where observations are recorded under different constraints. Furthermore, we show that, by doing so, we are able to learn representations of movement that generalise over constraints and can predict behaviour under new constraints. In our experiments, we test the algorithms on systems of varying size and complexity, and show that the novel approaches give significant improvements in performance compared with standard policy learning approaches that are naive to the effect of constraints. Finally, we illustrate the utility of the approaches for learning from human motion capture data and transferring behaviour to several robotic platforms

Non-rigid medical image registration with extended free form deformations: modelling general tissue transitions

Image registration seeks pointwise correspondences between the same or analogous objects in different images. Conventional registration methods generally impose continuity and smoothness throughout the image. However, there are cases in which the deformations may involve discontinuities. In general, the discontinuities can be of different types, depending on the physical properties of the tissue transitions involved and boundary conditions. For instance, in the respiratory motion the lungs slide along the thoracic cage following the tangential direction of their interface. In the normal direction, however, the lungs and the thoracic cage are constrained to be always in contact but they have different material properties producing different compression or expansion rates. In the literature, there is no generic method, which handles different types of discontinuities and considers their directional dependence. The aim of this thesis is to develop a general registration framework that is able to correctly model different types of tissue transitions with a general formalism. This has led to the development of the eXtended Free Form Deformation (XFFD) registration method. XFFD borrows the concept of the interpolation method from the eXtended Finite Element method (XFEM) to incorporate discontinuities by enriching B-spline basis functions, coupled with extra degrees of freedom. XFFD can handle different types of discontinuities and encodes their directional-dependence without any additional constraints. XFFD has been evaluated on digital phantoms, publicly available 3D liver and lung CT images. The experiments show that XFFD improves on previous methods and that it is important to employ the correct model that corresponds to the discontinuity type involved at the tissue transition. The effect of using incorrect models is more evident in the strain, which measures mechanical properties of the tissues

Engineering Data Compendium. Human Perception and Performance, Volume 1

The concept underlying the Engineering Data Compendium was the product an R and D program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design of military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by system designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is Volume 1, which contains sections on Visual Acquisition of Information, Auditory Acquisition of Information, and Acquisition of Information by Other Senses

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

Extraktion von Kontextinformationen zur Analyse von Nutzerströmen

Kontextadaptive Dienste ermitteln auf Basis von Sensordaten die aktuelle Situation des Nutzers. Dabei zählt neben Zeit, Identität und Aktivität vor allem der Aufenthaltsort zu den primären Kontextinformationen. Gerade Gebäude- und Infrastrukturbetreiber haben höchstes Interesse an solchen Daten, bekommen jedoch nur selten Zugriff darauf. Eine vielversprechende Alternative stellt das Aufzeichnen und Analysieren von Bluetooth- bzw. WLAN-Signalen dar, welches sich auch ohne Einverständnis der Nutzer realisieren lässt. Einige Startup-Unternehmen bieten mit dieser Strategie bereits Kundenanalysen an, jedoch fehlen wissenschaftliche Studien über die tatsächliche Güte solcher Verfahren. Zudem stellt die Extraktion primärer Kontextinformationen eine bisher ungelöste Herausforderung dar, da die aufgezeichneten Daten ein hohes Rauschen und eine geringe Frequenz aufweisen. Die vorliegende Arbeit geht dieses Problem der Datenunsicherheit an. So wird im ersten Teil untersucht, wie und welche Informationen aus Kommunikationsdaten gewonnen werden können. Diese werden anschließend zur Berechnung von Nutzerströmen in einem realen Szenario verwendet. Mithilfe einer bekannten Grundwahrheit können erstmals belastbare Aussagen über die Güte der evaluierten Verfahren getroffen werden. Die Ergebnisse zeigen, dass sich gerade WLAN-Daten zur Nutzerstromanalyse eignen, weshalb anschließend Simulationsmodelle von WLAN-Beobachtungen entwickelt werden. Dabei wird erstmals neben der Zeit auch die Aktivität und das Endgerät berücksichtigt. Der zweite Teil beschäftigt sich mit der Extraktion der Ortsinformation aus WLAN-Daten, die von mehreren Messknoten aufgezeichnet werden. Hierbei werden zunächst geometrische Ansätze zur Platzierung solcher Knoten vorgestellt. Auf Basis der Ergebnisse werden Messknoten installiert, um Bewegungspfade mobiler Nutzer im Gebäude zu ermitteln. Es zeigt sich, dass deterministische Methoden nicht die nötige Genauigkeit liefern und probabilistische Verfahren andere Nachteile aufweisen. Deshalb wird der State-Partikel-Filter als neuartige hybride Variante vorgestellt, die im Rahmen der Evaluation bessere Ergebnisse in Bezug auf anerkannte Metriken erreicht. Im dritten Teil wird die Extraktion von Aktivitäts- und Identitätsinformationen untersucht. Hierzu wird ein modifiziertes Verfahren zur Bestimmung von Ruhe- und Bewegungsphasen vorgestellt, das im Vergleich zu verwandten Arbeiten genauer ist. Auf Basis aller verfügbaren Informationen wird schließlich zur Identitätserkennung eine Klassifizierung von Nutzerrollen durchgeführt. Anders als in bisherigen Arbeiten findet so eine komplette Extraktion primärer Kontextinformationen aus aufgezeichneten Kommunikationsdaten statt.Context-aware services mainly estimate a user's current situation using sensor data. The well-known primary types of context information are time, identity, activity, and above all, location. Building managers and infrastructure providers are particularly interested in such data. However, they hardly get access to it. A promising alternative way without requiring users' permission for data access is to observe and analyse Bluetooth as well as Wi-Fi signals. Some start-ups already apply this technique in order to offer retail analytics. However, scientific studies providing information about the real performance of such approaches are still missing. Furthermore, extracting all of the primary types of context information is still challenging, due to the fact that the recorded data suffers from high noise and low sampling rates. This work deals with the afore-mentioned problems. Hence, the first chapter investigates, how and which kind of information can be inferred from captured communication data. Subsequently, this information is used to estimate user flows in a realistic scenario. With access to a known ground truth it becomes possible for the first time to reveal the real performance of the evaluated approaches. The results indicate that Wi-Fi captures are suitable for user flow analysis. Hence, models for simulating Wi-Fi based observations are developed considering time and novel aspects such as the user's activity and the device. The second chapter deals with the extraction of location information from Wi-Fi data which is captured by several distributed monitoring units. As a first step, geometric approaches for the placement of such units are presented. Based on the achieved results, Wi-Fi monitors are deployed in order to track and estimate trajectories of mobile users. In this connection, it is shown that deterministic methods do not reach the required accuracy and probabilistic approaches suffer from different drawbacks. Therefore, the so-called State-Particle-Filter is introduced as a novel hybrid variant returning better results in terms of well-adopted metrics within the performed evaluation. In the third chapter, the extraction of activity and identity information is investigated. Therefore, a modified approach for recognition of dwell times and motion periods is introduced which is shown to be more accurate than existing techniques. Finally, on the basis of all the available information, a user role classification task is performed for identity discovery. Thus, in contrast to other works, a complete extraction of the well-known primary types of context information is realized using captured communication data and the proposed methods