Pieterjan De Potter

Semantic patient information aggregation and medicinal decision support

Although the health care sector has already been subjected to a major computerization effort, this effort is often limited to the implementation of standalone systems which do not communicate with each other. Interoperability problems limit health care applications from achieving their full potential. In this paper, we propose the use of Semantic Web technologies to solve interoperability problems between data providers. Through the development of unifying health care ontologies, data from multiple health care providers can be aggregated, which can then be used as input for a decision support system. This way, more data is taken into account than a single health care provider possesses in his local setting. The feasibility of our approach is demonstrated by the creation of an end-to-end proof of concept, focusing on Belgian health care providers and medicinal decision support.

Semantic web technologies for video surveillance metadata

Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different modules typically use different standards, resulting in metadata interoperability problems. In this paper, we introduce the application of Semantic Web Technologies to overcome such problems. We present a semantic, layered metadata model and integrate it within a video surveillance system. Besides dealing with the metadata interoperability problem, the advantages of using Semantic Web Technologies and the inherent rule support are shown. A practical use case scenario is presented to illustrate the benefits of our novel approach.

Multi-modal time-of-flight based fire detection

This paper proposes two novel time-of-flight based fire detection methods for indoor and outdoor fire detection. The indoor detector is based on the depth and amplitude image of a time-of-flight camera. Using this multi-modal information, flames can be detected very accurately by fast changing depth and amplitude disorder detection. In order to detect the fast changing depth, depth differences between consecutive frames are accumulated over time. Regions which have multiple pixels with a high accumulated depth difference are labeled as candidate flame regions. Simultaneously, the amplitude disorder is also investigated. Regions with high accumulative amplitude differences and high values in all detail images of the amplitude image its discrete wavelet transform, are also labeled as candidate flame regions. Finally, if one of the depth and amplitude candidate flame regions overlap, fire alarm is given. The outdoor detector, on the other hand, only differs from the indoor detector in one of its multi-modal inputs. As depth maps are unreliable in outdoor environments, the outdoor detector uses a visual flame detector instead of the fast changing depth detection. Experiments show that the proposed detectors have an average flame detection rate of 94% with no false positive detections.

Video driven fire spread forecasting (f) using multi-modal LWIR and visual flame and smoke data

Being able to model and forecast the fire can help emergency services to work more efficiently and save lives. However, the calculations with current fire modeling techniques, such as CFD and zone models, still take too long and valuable time is often lost. Using the video driven fire spread forecasting methodology proposed in this paper, which is able to give real-time information about the state of the environment, model-based predictions of the future state of a fire can be improved and accelerated. By combining the information about the fire from models and real-time multi-modal LWIR and visual flame and smoke data an estimate of the fire can be produced that is better than could be obtained from using the model or the data alone.

Multi-sensor fire detection using visual and time-of-flight imaging

This paper proposes a novel multi-sensor fire detection method based on ordinary video images and the amplitude images of a time-of-flight camera. Using this multi-modal information, flame regions can be detected very accurately. Regions with high accumulative amplitude differences and high values in all detail images of the amplitude image its discrete wavelet transform, are labeled as candidate flame regions. Simultaneously, moving objects in the visual images are also investigated. Objects which possess the experimentally found low-cost flame features are also labeled as candidate flame region. Finally, if one of the visual and amplitude candidate flame regions overlap, fire alarm is given. Experiments show that the proposed detector has an average flame detection rate of 92% with no false positive detections.

Automatic GEO-MASHUP generation of outdoor activities

In this paper, we describe a novel approach for the automatic generation of a GEO-MASHUP related to a user his outdoor activities. Each mashup consists of online geotagged media resources related to the geographic keypoints where the outdoor activity was performed. In order to detect candidate keypoints, we search low-activity locations based on the travelling distance over time. Subsequently, we filter out route-specific keypoints (such as traffic lights) using online trajectory information. Finally, the remaining keypoints are fed to a set of social media web services to retrieve the geotagged media which summarizes the users activity. The GEO-MASHUP demonstrator, which is evaluated in real-world conditions, shows the feasibility of our novel approach.

Next generation assisting clinical applications by using semantic-aware electronic health records

The health care sector is no longer imaginable without electronic health records. However, since the original idea of electronic health records was focused on data storage and not on data processing, a lot of current implementations do not take full advantage of the opportunities provided by computerization. This paper introduces the Patient Summary Ontology for the representation of electronic health records and demonstrates the possibility to create next generation assisting clinical applications based on these semantic-aware electronic health records. Also, an architecture to interoperate with electronic health records formatted using other standards is presented.

Automatic geographic enrichment by multi-modal bike sensing

This paper focuses on the automatic geo-annotation of road/terrain types by collaborative bike sensing. The proposed terrain classification system is mainly based on the analysis of volunteered geographic information gathered by cyclists. By using participatory accelerometer and GPS sensor data collected from the cyclists’ smartphones, which is enriched with image data from geographic web services or the smartphone camera, the proposed system is able to distinguish between 6 different terrain types. For the classification of the multi-modal bike data, the system employs a random decision forest (RDF), which compared favorably for the geo-annotation task against different classification algorithms. The system classifies the features of every instance of road (over a 5 seconds interval) and maps the results onto the corresponding GPS coordinates. Finally, based on all the collected instances, we can annotate geographic maps with the terrain types, create more advanced route statistics and facilitate geo-based recommender systems. The accuracy of the bike sensing system is 92 % for 6-class terrain classification. For the 2-class on-road/off-road classification an accuracy of 97 % is achieved, almost six percent above the state-of-the-art in this domain.

Automatic Geographic Enrichment by Multi-modal Bike Sensing

This paper focuses on the automatic geo-annotation of road/terrain types by collaborative bike sensing. The proposed terrain classification system is mainly based on the analysis of volunteered geographic information gathered by cyclists. By using participatory accelerometer and GPS sensor data collected from the cyclists’ smartphones, which is enriched with image data from geographic web services or the smartphone camera, the proposed system is able to distinguish between 6 different terrain types. For the classification of the multi-modal bike data, the system employs a random decision forest (RDF), which compared favorably for the geo-annotation task against different classification algorithms. The system classifies the features of every instance of road (over a 5 seconds interval) and maps the results onto the corresponding GPS coordinates. Finally, based on all the collected instances, we can annotate geographic maps with the terrain types, create more advanced route statistics and facilitate geo-based recommender systems. The accuracy of the bike sensing system is 92 % for 6-class terrain classification. For the 2-class on-road/off-road classification an accuracy of 97 % is achieved, almost six percent above the state-of-the-art in this domain.

Visitor-art interaction by motion path detection

This paper describes a method for video-based motion path detection which is applied in the creation of an interactive artwork. The proposed algorithm, based on the Hough transform, detects parametric motion trajectories in real-time (10 fps). In order to detect peoples motion under nonstatic background object occlusion we have also developed a video segmentation technique. The proposed interaction system adopts top-down camera view to extract spatiotemporal motion trajectories and discern predefined patterns of movement thus enabling the creation of new artistic choreographies. We present test results that illustrate the effectiveness of our method and discuss the practical applicability of our approach in other domains.