Dr. Adam W. M. van Eekeren Profile

Dr. Adam W. M. van Eekeren

Research Scientist at TNO

SPIE Involvement:

Author

Area of Expertise:

Image enhancement , turbulence compensation , super-resolution reconstruction , object detection

Publications (22)

Proceedings Article | 6 October 2017 Presentation + Paper

Dynamic turbulence mitigation with large moving objects

Robert P. Nieuwenhuizen, Adam W. van Eekeren, Judith Dijk, Klamer Schutte

Proceedings Volume 10433, 104330S (2017) https://doi.org/10.1117/12.2277840

KEYWORDS: Turbulence, Motion estimation, Optical flow, Video, Visualization, Image restoration, Cameras, Image processing, Range imaging, Visible radiation

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Proceedings Article | 13 October 2015 Paper

Vehicle tracking in wide area motion imagery from an airborne platform

Adam van Eekeren, Jasper van Huis, Pieter Eendebak, Jan Baan

Proceedings Volume 9648, 96480I (2015) https://doi.org/10.1117/12.2196392

KEYWORDS: Video, Sensors, Image analysis, Roads, Detection and tracking algorithms, Cameras, 3D modeling, Image resolution, Content addressable memory, Image sensors

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Proceedings Article | 7 October 2014 Paper

Focus-of-attention for human activity recognition from UAVs

G. Burghouts, A. van Eekeren, J. Dijk

Proceedings Volume 9249, 92490T (2014) https://doi.org/10.1117/12.2067569

KEYWORDS: Video, Unmanned aerial vehicles, Sensors, Cameras, Human-machine interfaces, Video surveillance, Classification systems, Intelligence systems, Detection and tracking algorithms, Imaging systems

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Proceedings Article | 7 October 2014 Paper

Detection and tracking of humans from an airborne platform

Adam van Eekeren, Judith Dijk, Gertjan Burghouts

Proceedings Volume 9249, 92490S (2014) https://doi.org/10.1117/12.2067568

KEYWORDS: Video, Sensors, Switches, Motion detection, Zoom lenses, Detection and tracking algorithms, Automatic tracking, Cameras, Electronic filtering, Visualization

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Airborne platforms are recording large amounts of video data. Extracting the events which are needed to see is a time-demanding task for analysts. The reason for this is that the sensors record hours of video data in which only a fraction of the footage contains events of interest. For the analyst, it is hard to retrieve such events from the large amounts of video data by hand. A way to extract information more automatically from the data is to detect all humans within the scene. This can be done in a real-time scenario (both on-board as on the ground station) for strategic and tactical purposes and in an offline scenario where the information is analyzed after recording to acquire intelligence (e.g. a daily life pattern). In this paper, we evaluate three different methods for object detection from a moving airborne platform. The first one is a static person detection algorithm. The main advantage of this method is that it can be used on single frames, and therefor does not depend on the stabilization of the platform. The main disadvantage of this method is that the number of pixels needed for the detection is pretty large. The second method is based on detection of motion-in-motion. Here the background is stabilized, and clusters of pixels that move with respect to this stabilized background are detected as moving object. The main advantage is that all moving objects are detected, the main disadvantage is that it heavily depends on the quality of the stabilization. The third method combines both previous detection methods. The detections are tracked using a histogram-based tracker, so that missed detections can be filled in and a trajectory of all objects can be determined. We demonstrate the tracking performance using the three different detections methods on the publicly available UCF-ARG aerial dataset. The performance is evaluated for two human actions (running and digging) and varying object sizes. It is shown that a combined detection approach (static person detection and motion-in-motion detection) gives better tracking results for both human actions than using one of the detectors alone. Furthermore it can be concluded that the minimal height of humans must be 20 pixels to guarantee a good tracking performance.

Proceedings Article | 7 October 2014 Paper

Turbulence mitigation methods and their evaluation

Adam van Eekeren, Judith Dijk, Klamer Schutte

Proceedings Volume 9249, 92490O (2014) https://doi.org/10.1117/12.2067327

KEYWORDS: Turbulence, Infrared imaging, Visualization, Motion estimation, Infrared radiation, Infrared cameras, Cameras, Infrared detectors, Atmospheric sensing, Scintillation

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Proceedings Article | 29 May 2014 Paper

Evaluation of turbulence mitigation methods

Adam W. van Eekeren, Claudia Huebner, Judith Dijk, Klamer Schutte, Piet B. Schwering

Proceedings Volume 9071, 907113 (2014) https://doi.org/10.1117/12.2050314

KEYWORDS: Turbulence, Deconvolution, Cameras, Image processing, Atmospheric turbulence, Visualization, Image segmentation, Image quality, Image registration, Motion detection

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Proceedings Article | 15 October 2013 Paper

Image processing in aerial surveillance and reconnaissance: from pixels to understanding

Judith Dijk, Adam W. van Eekeren, Olga Rajadell Rojas, Gertjan Burghouts, Klamer Schutte

Proceedings Volume 8897, 88970A (2013) https://doi.org/10.1117/12.2029591

KEYWORDS: Image enhancement, Cameras, Image processing, Unmanned aerial vehicles, Sensors, 3D modeling, Motion estimation, Surveillance, Detection and tracking algorithms, Motion models

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Proceedings Article | 5 June 2013 Paper

Quantitative evaluation of turbulence compensation

Adam W. van Eekeren, Klamer Schutte, Judith Dijk, Piet B. Schwering

Proceedings Volume 8706, 87060K (2013) https://doi.org/10.1117/12.2015371

KEYWORDS: Turbulence, Atmospheric turbulence, Cameras, Image registration, Image quality, Image processing, Quality measurement, Visualization, Video, Optical flow

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Proceedings Article | 17 May 2013 Paper

Landing spot selection for UAV emergency landing

P. Eendebak, A. W. van Eekeren, R. J. den Hollander

Proceedings Volume 8741, 874105 (2013) https://doi.org/10.1117/12.2017910

KEYWORDS: Unmanned aerial vehicles, Cameras, Video, Motion estimation, Infrared radiation, Infrared imaging, Visualization, Image segmentation, RGB color model, Motion detection

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SPIE Journal Paper | 4 December 2012

Evaluation of intensified image enhancement through conspicuity and triangle orientation discrimination measures

Judith Dijk, Adam van Eekeren, Alexander Toet, Richard den Hollander, Klamer Schutte, Ad van Heijningen, Piet Bijl

OE, Vol. 52, Issue 04, 041105, (December 2012) https://doi.org/10.1117/12.10.1117/1.OE.52.4.041105

KEYWORDS: Image enhancement, Cameras, Denoising, Image intensifiers, Image processing, Visualization, Analog electronics, Image contrast enhancement, Target detection, Image fusion

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Proceedings Article | 19 November 2012 Paper

An overview of turbulence compensation

Klamer Schutte, Adam W. van Eekeren, Judith Dijk, Piet B. Schwering, Miranda van Iersel, Niek Doelman

Proceedings Volume 8542, 85420O (2012) https://doi.org/10.1117/12.981942

KEYWORDS: Turbulence, Wavefronts, Adaptive optics, Image processing, Deconvolution, Modulators, Visualization, Telescopes, Actuators, Atmospheric turbulence

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Proceedings Article | 8 June 2012 Paper

Single image super resolution via sparse reconstruction

Maarten Kruithof, Adam van Eekeren, Judith Dijk, Klamer Schutte

Proceedings Volume 8365, 83650F (2012) https://doi.org/10.1117/12.919036

KEYWORDS: Image resolution, Associative arrays, Super resolution, Reconstruction algorithms, Sensors, Optical resolution, Temporal resolution, Cameras, Image compression, Cell phones

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Proceedings Article | 18 May 2012 Paper

Turbulence compensation: an overview

Adam W. van Eekeren, Klamer Schutte, Judith Dijk, Piet B. Schwering, Miranda van Iersel, Niek Doelman

Proceedings Volume 8355, 83550Q (2012) https://doi.org/10.1117/12.918544

KEYWORDS: Turbulence, Wavefronts, Adaptive optics, Image processing, Deconvolution, Modulators, Visualization, Telescopes, Actuators, Atmospheric turbulence

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Proceedings Article | 18 May 2012 Paper

Measuring the performance of super-resolution reconstruction algorithms

Judith Dijk, Klamer Schutte, Adam van Eekeren, Piet Bijl

Proceedings Volume 8355, 835515 (2012) https://doi.org/10.1117/12.919225

KEYWORDS: Super resolution, Reconstruction algorithms, Image resolution, Modulation transfer functions, Lawrencium, Signal to noise ratio, Image enhancement, Minimum resolvable temperature difference, Image processing, Cameras

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Proceedings Article | 18 May 2012 Paper

Patch-based local turbulence compensation in anisoplanatic conditions

Adam W. van Eekeren, Maarten Kruithof, Klamer Schutte, Judith Dijk, Miranda van Iersel, Piet B. Schwering

Proceedings Volume 8355, 83550T (2012) https://doi.org/10.1117/12.918545

KEYWORDS: Turbulence, Cameras, Image registration, Atmospheric turbulence, Image processing, Visualization, Super resolution, Optical flow, Deconvolution, Atmospheric optics

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Proceedings Article | 7 May 2012 Paper

Streaming video-based 3D reconstruction method compatible with existing monoscopic and stereoscopic endoscopy systems

Henri Bouma, Wannes van der Mark, Pieter Eendebak, Sander Landsmeer, Adam W. van Eekeren, Frank ter Haar, F. Pieter Wieringa, Jean-Paul van Basten

Proceedings Volume 8371, 837112 (2012) https://doi.org/10.1117/12.918592

KEYWORDS: Endoscopy, 3D modeling, Volume rendering, Endoscopes, Video, 3D image processing, 3D video streaming, Surgery, Cameras, Clouds

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Proceedings Article | 5 May 2012 Paper

Precise local blur estimation based on the first-order derivative

Henri Bouma, Judith Dijk, Adam van Eekeren

Proceedings Volume 8399, 839904 (2012) https://doi.org/10.1117/12.918600

KEYWORDS: Stochastic processes, Super resolution, Image restoration, Turbulence, Error analysis, Image analysis, Point spread functions, Cameras, Video, Signal to noise ratio

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Proceedings Article | 21 May 2011 Paper

Time-varying phase diversity turbulence compensation

Adam W. van Eekeren, Klamer Schutte, Judith Dijk, Piet B. Schwering

Proceedings Volume 8012, 80120D (2011) https://doi.org/10.1117/12.884580

KEYWORDS: Turbulence, Wavefronts, Modulation, Point spread functions, Cameras, Modulators, Atmospheric turbulence, Image analysis, Visualization, Image processing

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Proceedings Article | 22 April 2010 Paper

Performance evaluation of image enhancement techniques on night vision imagery

Judith Dijk, Piet Bijl, Adam W.M. van Eekeren

Proceedings Volume 7662, 766207 (2010) https://doi.org/10.1117/12.853396

KEYWORDS: Cameras, Image enhancement, Electron multiplying charge coupled devices, Light sources and illumination, Denoising, Super resolution, Visualization, Image contrast enhancement, Contrast sensitivity, Image intensifiers

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Proceedings Article | 5 May 2009 Paper

Performance study on point target detection using super-resolution reconstruction

Judith Dijk, Adam W. van Eekeren, Klamer Schutte, Dirk-Jan de Lange, Lucas van Vliet

Proceedings Volume 7335, 73350M (2009) https://doi.org/10.1117/12.819093

KEYWORDS: Target detection, Super resolution, Lawrencium, Reconstruction algorithms, Zoom lenses, Detection and tracking algorithms, Image registration, Cameras, Motion models, Infrared sensors

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SPIE Journal Paper | 1 September 2008

Superresolution reconstruction for moving point target detection

Judith Dijk, Adam van Eekeren, Klamer Schutte, Dirk-Jan de Lange, Lucas van Vliet

OE, Vol. 47, Issue 09, 096401, (September 2008) https://doi.org/10.1117/12.10.1117/1.2977790

KEYWORDS: Target detection, Cameras, Lawrencium, Super resolution, Missiles, Zoom lenses, Detection and tracking algorithms, Image registration, Optical engineering, Reconstruction algorithms

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Proceedings Article | 7 May 2007 Paper

Point target detection using super-resolution reconstruction

Judith Dijk, Adam W. M. van Eekeren, Klamer Schutte, Dirk-Jan de Lange

Proceedings Volume 6566, 65660U (2007) https://doi.org/10.1117/12.725074

KEYWORDS: Target detection, Super resolution, Reconstruction algorithms, Signal to noise ratio, Cameras, Lawrencium, Detection and tracking algorithms, Image resolution, Image enhancement, Resolution enhancement technologies

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Showing 5 of 22 publications

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