I am a professional who prefers to work on applied science, between fundamental research and application. The main focus of my research is application of camera systems for different applications, and image processing needed for this. I am interested in research and development of new emerging technologies such as Artificial Intelligence. This includes obtaining insight in what technology developments are needed to achieve new capabilities within different applications. Next to that I apply these technologies for electro-optical systems for defense applications.
Awards
• NATO STO Excellence Award for SET 226 on Turbulence Mitigation 2020
• NATO STO Excellence Award for SET 232 on Compressive sensing & computational imaging, 2020
• SPIE conference & community champion 2020
• SPIE conference & community champion 2021
Awards
• NATO STO Excellence Award for SET 226 on Turbulence Mitigation 2020
• NATO STO Excellence Award for SET 232 on Compressive sensing & computational imaging, 2020
• SPIE conference & community champion 2020
• SPIE conference & community champion 2021
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The trial, which serves as the foundation for subsequent data analysis, encompassed a multitude of scenarios designed to challenge the limits of computational imaging technologies. The diverse set of targets, each with its unique set of challenges, allows for the examination of system performance across various environmental and operational conditions.
Of all available snapshots, only the best and most representative snapshots should be selected for the operator. In this paper, we present two different approaches for snapshot selection from a vessel track. The first is based on directional track information, and the second on the snapshot appearance. We present results for both these methods on IR recordings, containing vessels with different track patterns in a harbor scenario.
In our current research, we propose a ‘maritime detection framework 2.0’, in which multi-platform sensors are combined with detection algorithms. In this paper, we present a comparison of detection algorithms for EO sensors within our developed framework and quantify the performance of this framework on representative data.
Automatic detection can be performed within the proposed framework in three ways: 1) using existing detectors, such as detectors based on movement or local intensities; 2) using a newly developed detector based on saliency on the scene level; and 3) using a state-of-the-art deep learning method. After detection, false alarms are suppressed using consecutive tracking approaches. The performance of these detection methods is compared by evaluating the detection probability versus the false alarm rate for realistic multi-sensor data.
New types of maritime targets require new target detection strategies. Combining new detection strategies with existing tracking technologies shows potential increase in detection performance of the complete framework.
Trackers make errors, for example, due to inaccuracies in detection, or motion that is not modeled correctly. Instead of improving this tracking using the limited information available from a single measurement, we propose a method where tracks are merged at a later stage, using information over a small interval. This merging is based on spatiotemporal matching. To limit incorrect connections, unlikely connections are identified and excluded. For this we propose two different approaches: spatiotemporal cost functions are used to exclude connections with unlikely motion and appearance cost functions are used to exclude connecting tracks of dissimilar objects. Next to this, spatiotemporal cost functions are also used to select tracks for merging. For the appearance filtering we investigated different descriptive features and developed a method for indicating similarity between tracks. This method handles variations in features due to noisy detections and changes in appearance.
We tested this method on real data with nine different targets. It is shown that track merging results in a significant reduction in number of tracks per ship. With our method we significantly reduce incorrect track merges that would occur using naïve merging functions.
In this paper we show results of this processing chain for sea scenarios using our TNO turbulence mitigation method. Ship data is processed using the algorithm proposed above and the results are analyzed by both human observation and by image analysis. The improvement of the imagery is qualitatively shown by examining details which cannot be seen without processing and can be seen with processing. Quantitatively, the improvement is related to the energy per spatial frequency in the original and processed images and the signal to noise improvement. This provides a model for the improvement of the results, and is related to the improvement of the classification and identification range. The results show that with this novel approach the classification and identification range of ships is improved.
Real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall
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