4,609 research outputs found

    Interactive Information Visualization for Sensemaking in Power Grid Supervisory Systems

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    Operators of power grid supervisory control systems have to gather information from a wide variety of views to build situation awareness. Findings from a conducted field study show that this task is challenging and cogni-tively demanding. Visualization research for power grid supervisory control systems has focused on developing new visualization techniques for representing one aspect of the power system data. Little work has been done to demon-strate how information visualization techniques can sup-port the operator in the sensemaking process to achieve situation awareness. To fill this gap, and with support from a field study, we propose solutions based on multiple and coordinated views, visual interactive filtering and parallel coordinates

    Algorithmic issues in visual object recognition

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    This thesis is divided into two parts covering two aspects of research in the area of visual object recognition. Part I is about human detection in still images. Human detection is a challenging computer vision task due to the wide variability in human visual appearances and body poses. In this part, we present several enhancements to human detection algorithms. First, we present an extension to the integral images framework to allow for constant time computation of non-uniformly weighted summations over rectangular regions using a bundle of integral images. Such computational element is commonly used in constructing gradient-based feature descriptors, which are the most successful in shape-based human detection. Second, we introduce deformable features as an alternative to the conventional static features used in classifiers based on boosted ensembles. Deformable features can enhance the accuracy of human detection by adapting to pose changes that can be described as translations of body features. Third, we present a comprehensive evaluation framework for cascade-based human detectors. The presented framework facilitates comparison between cascade-based detection algorithms, provides a confidence measure for result, and deploys a practical evaluation scenario. Part II explores the possibilities of enhancing the speed of core algorithms used in visual object recognition using the computing capabilities of Graphics Processing Units (GPUs). First, we present an implementation of Graph Cut on GPUs, which achieves up to 4x speedup against compared to a CPU implementation. The Graph Cut algorithm has many applications related to visual object recognition such as segmentation and 3D point matching. Second, we present an efficient sparse approximation of kernel matrices for GPUs that can significantly speed up kernel based learning algorithms, which are widely used in object detection and recognition. We present an implementation of the Affinity Propagation clustering algorithm based on this representation, which is about 6 times faster than another GPU implementation based on a conventional sparse matrix representation

    Traffic Alert and Collision Avoidance System (TCAS): Cockpit Display of Traffic Information (CDTI) investigation. Phase 1: Feasibility study

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    The possibility of the Threat Alert and Collision Avoidance System (TCAS) traffic sensor and display being used for meaningful Cockpit Display of Traffic Information (CDTI) applications has resulted in the Federal Aviation Administration initiating a project to establish the technical and operational requirements to realize this potential. Phase 1 of the project is presented here. Phase 1 was organized to define specific CDTI applications for the terminal area, to determine what has already been learned about CDTI technology relevant to these applications, and to define the engineering required to supply the remaining TCAS-CDTI technology for capacity benefit realization. The CDTI applications examined have been limited to those appropriate to the final approach and departure phases of flight

    eStorys: A visual storyboard system supporting back-channel communication for emergencies

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    This is the post-print version of the final paper published in Journal of Visual Languages & Computing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.In this paper we present a new web mashup system for helping people and professionals to retrieve information about emergencies and disasters. Today, the use of the web during emergencies, is confirmed by the employment of systems like Flickr, Twitter or Facebook as demonstrated in the cases of Hurricane Katrina, the July 7, 2005 London bombings, and the April 16, 2007 shootings at Virginia Polytechnic University. Many pieces of information are currently available on the web that can be useful for emergency purposes and range from messages on forums and blogs to georeferenced photos. We present here a system that, by mixing information available on the web, is able to help both people and emergency professionals in rapidly obtaining data on emergency situations by using multiple web channels. In this paper we introduce a visual system, providing a combination of tools that demonstrated to be effective in such emergency situations, such as spatio/temporal search features, recommendation and filtering tools, and storyboards. We demonstrated the efficacy of our system by means of an analytic evaluation (comparing it with others available on the web), an usability evaluation made by expert users (students adequately trained) and an experimental evaluation with 34 participants.Spanish Ministry of Science and Innovation and Universidad Carlos III de Madrid and Banco Santander

    Look Before You Leap: Improving the Users’ Ability to Detect Fraud in Electronic Marketplaces

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    Reputation systems in current electronic marketplaces can easily be manipulated by malicious sellers in order to appear more reputable than appropriate. We conducted a controlled experiment with 40 UK and 41 German participants on their ability to detect malicious behavior by means of an eBay-like feedback profile versus a novel interface involving an interactive visualization of reputation data. The results show that participants using the new interface could better detect and understand malicious behavior in three out of four attacks (the overall detection accuracy 77% in the new vs. 56% in the old interface). Moreover, with the new interface, only 7% of the users decided to buy from the malicious seller (the options being to buy from one of the available sellers or to abstain from buying), as opposed to 30% in the old interface condition
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