Uncertainty-aware video visual analytics of tracked moving objects

Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making we gather uncertainties introduced by the computer vision step communicate these information to users through uncertainty visualization and grant fuzzy hypothesis formulation to interact with the machine. Finally we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009

Variations and Application Conditions Of the Data Type »Image« - The Foundation of Computational Visualistics

Few years ago, the department of computer science of the University Magdeburg invented a completely new diploma programme called 'computational visualistics', a curriculum dealing with all aspects of computational pictures. Only isolated aspects had been studied so far in computer science, particularly in the independent domains of computer graphics, image processing, information visualization, and computer vision. So is there indeed a coherent domain of research behind such a curriculum? The answer to that question depends crucially on a data structure that acts as a mediator between general visualistics and computer science: the data structure "image". The present text investigates that data structure, its components, and its application conditions, and thus elaborates the very foundations of computational visualistics as a unique and homogenous field of research. Before concentrating on that data structure, the theory of pictures in general and the definition of pictures as perceptoid signs in particular are closely examined. This includes an act-theoretic consideration about resemblance as the crucial link between image and object, the communicative function of context building as the central concept for comparing pictures and language, and several modes of reflection underlying the relation between image and image user. In the main chapter, the data structure "image" is extendedly analyzed under the perspectives of syntax, semantics, and pragmatics. While syntactic aspects mostly concern image processing, semantic questions form the core of computer graphics and computer vision. Pragmatic considerations are particularly involved with interactive pictures but also extend to the field of information visualization and even to computer art. Four case studies provide practical applications of various aspects of the analysis

Technological innovations in biomedical training and practice

As we become more integrated into a global world, technological advances and teaching innovation that are grounded in Science have become crucial. Rapid advancements in science education and information technology provide promising resources that require many academic disciplines to work together. Developing new tools and defining new methodologies to share educational experiences, including empirical studies that support their efficiency, constitute a promising approach to improve Health Sciences. The aim of this session is to encourage and enable the exchange of information related with the advance and support of Health Science Education. In this paper the authors summarize the recent advances in technological innovations in biomedical training and practice. Most of the main trends in this field are reviewed, including: training in health sciences through a variety of resources such as computer simulations, stereoscopic visualization systems with augmented reality glasses, computer platforms for managing and using resources and documents; the generation of three-dimensional images developed with commercial software for 3D reconstruction; medical and surgical simulation using Virtual Reality (RV) and Augmented Reality (AR); the role of stereoscopic vision systems in the health sciences; and the use of teaching medical material reconstructed with 3D printers.info:eu-repo/semantics/publishedVersio

Visualization as a stimulus domain for vision science

Traditionally, vision science and information/data visualization have interacted by using knowledge of human vision to help design effective displays. It is argued here, however, that this interaction can also go in the opposite direction: the investigation of successful visualizations can lead to the discovery of interesting new issues and phenomena in visual perception. Various studies are reviewed showing how this has been done for two areas of visualization, namely, graphical representations and interaction, which lend themselves to work on visual processing and the control of visual operations, respectively. The results of these studies have provided new insights into aspects of vision such as grouping, attentional selection and the sequencing of visual operations. More generally yet, such results support the view that the perception of visualizations can be a useful domain for exploring the nature of visual cognition, inspiring new kinds of questions as well as casting new light on the limits to which information can be conveyed visually

Building the eResearch Marine Science Information Infrastructure

The talk will introduce the Australian Oceans Data Network (AODN) and two large associated projects, namely the BlueNet and the eMII initiatives. The AODN is a distributed data hosting and delivery infrastructure which provides a one-stop shop for marine science researchers. It encompasses data from all sectors, i.e. government, academic, public and commercial, but most of the hosting nodes are within the Australian Oceans Data Centre Joint Facility. The AODN is a sustainable and extensible solution based on standards, interoperability, and customized data management for particular marine science data streams. It provides the tools and services needed for end-to-end dataflows, and the associated transitions between data, information and knowledge. BlueNet is a new Australian Marine Science Data Management initiative funded by DEST through the SII program, and builds on the vision of the AODN. Aimed at optimising use of the marine science research output from Australian Universities, BlueNet is establishing sustainable processes for better managing marine science data currently languishing in the academic and related sectors. This is a multi-pronged strategy, building and facilitating a data-sharing culture as well as developing and delivering the infrastructure, through the AODN, that is needed to effectively manage, describe and deliver heterogeneous marine science data. BlueNet offers universities a distributed data archiving, hosting and delivery solution which maximizes data discovery and re-use. The infrastructure includes tools and standards-based services that ensure interoperability, and provide customized discovery, downloading, visualization and manipulation of data. The eMII (eMarine Information Infrastructure) project is the data management component of the NCRIS-funded Integrated Marine Observing System (IMOS). eMII will further build on the AODN, harnessing the AODN to facilitate management of the considerable diversity and volume of marine science data flowing from IMOS over the next 5 years

A system for synthetic vision and augmented reality in future flight decks

Rockwell Science Center is investigating novel human-computer interaction techniques for enhancing the situational awareness in future flight decks. One aspect is to provide intuitive displays that provide the vital information and the spatial awareness by augmenting the real world with an overlay of relevant information registered to the real world. Such Augmented Reality (AR) techniques can be employed during bad weather scenarios to permit flying in Visual Flight Rules (VFR) in conditions which would normally require Instrumental Flight Rules (IFR). These systems could easily be implemented on heads-up displays (HUD). The advantage of AR systems vs. purely synthetic vision (SV) systems is that the pilot can relate the information overlay to real objects in the world, whereas SV systems provide a constant virtual view, where inconsistencies can hardly be detected. The development of components for such a system led to a demonstrator implemented on a PC. A camera grabs video images which are overlaid with registered information. Orientation of the camera is obtained from an inclinometer and a magnetometer; position is acquired from GPS. In a possible implementation in an airplane, the on-board attitude information can be used for obtaining correct registration. If visibility is sufficient, computer vision modules can be used to fine-tune the registration by matching visual cues with database features. This technology would be especially useful for landing approaches. The current demonstrator provides a frame-rate of 15 fps, using a live video feed as background with an overlay of avionics symbology in the foreground. In addition, terrain rendering from a 1 arc sec. digital elevation model database can be overlaid to provide synthetic vision in case of limited visibility. For true outdoor testing (on ground level), the system has been implemented on a wearable computer