Research Projects (2005)

CashFlow is an open source flow visualization system extending Open Inventor and Coin3D toolkit by System In Motion with new features for visualization and rendering of arbitrary grids and vector data sets.

Breast cancer is the most common cancer among women. In the European Community breast cancer is diagnosed in 157,000 cases and kills 70 000 annually. Early detection is essential to increase the surveillance rate. Therefore a lot of work has been done in recent years to enhance the prediction quality of the three most promising image modalities for breast cancer detection: X-ray imaging is used for screening of woman older than 50 years. Younger woman with dense breasts can be examined with sonography. Functional magnet resonance imaging gives insight in the interaction of different tissue types with the contrast agent.

Radiologists face the difficulty of combining the information obtained by non invasive methods to reduce the need for biopsy and surgery. In this project methods to fuse X-ray and fMR images will be researched.

Partner: Image Diagnost

Multimedia data has a rich and complex structure in terms of inter- and intra-document references and can be an extremely valuable source of information. However, this potential is severely limited until and unless effective methods for semantic extraction and semantic-based cross-media exploration and retrieval can be devised. Today’s leading-edge techniques in this area are working well for low-level feature extraction (e.g. colour histograms), are focussing on narrow aspects of isolated collections of multimedia data, and are dealing only with single media types. MISTRAL follows the following lines of radically new research: MISTRAL will extract a large variety of semantically relevant metadata from one media type and integrate it closely with semantic concepts derived from other media types. Eventually, the results from this cross-media semantic integration will also be fed back to the semantic extraction processes of the different media types so as to enhance the quality of the results of these processes. MISTRAL will focus on most innovative, semantic-based cross-media exploration and retrieval techniques employing concepts at different semantic levels. MISTRAL addresses the specifics of multimedia data in the global, networked context employing semantic web technologies. The MISTRAL results for semantic-based multimedia retrieval will contribute to a significant improvement of today’s human-computer interaction in multimedia retrieval and exploration applications. New types of functionalities include but are not limited to:

• cross-media-based automatic detection of objects in multimedia data: For example, if a video contains an audio stream with barking together with a particular constellation of video features, the system can automatically consider the features in the video as an object "dog".
• semantic-enriched cross-media queries: A sample query could be "find all videos with a barking dog in the background and playing children in the foreground".
• cross-media synchronisation: The idea is to synchronize independent types of media according to the extracted semantic concepts. For example, if users see somebody walking in a video, they should also hear footfall from an audio.

Development of Computer Vision Algorithms for Robust Object Detection and Tracking on an Embedded DSP Platform. FREQUENTIS was working on an embedded platform called "TRICam". The platform is capable of acting as a high quality Motion-JPEG or MPEG4 Encoder or Decoder; furthermore, it can perform a set of selected image processing algorithms for object detection and tracking in real-time. The focus was mainly put on traffic monitoring, vehicle detection and tracking. The goal was to also deal with various algorithms for video surveillance in public places and public transport.

Vehicles driving the wrong way down a motorway represent a serious source of danger. In the year 2005 in Austria, 521 vehicles were counted driving in the wrong direction on motorways. 8 people died due to wrong-way driver accidents.

Immediate detection of a vehicle driving in the wrong direction could help preventing serious accidents by warning the oncoming vehicles (via traffic telematic systems or radio announcements) and by alarming the police. To guarantee immediate detection, one would have to observe every access ramp and every place where a car could turn. Thus a low-cost monitoring system capable of detecting wrong-way drivers and forwarding an alarm to a central traffic control station is needed. Image based detection suffers from one main problem: In case a large vehicle (truck, bus etc.) throws a shadow to the opposite lane of a motorway, that shadow moves against the direction of traffic and may cause a false detection. With a monocular camera system (one single camera), this problem can only be eliminated by large computational costs.

The main aim of VITUS project is to build and implement a prototype for an automatic video image analysis system in order to increase safety in tunnel roads. A feasibility study about video image analysis in tunnels was carried out, and the implementation of the prototype and evaluation of the system is work on going. Experiments on real sequences using innovative image processing algorithms display promising results.