Short scientific CV

Overview of research activities in the Image Analysis Group at TU Dortmund (since April 2010)
(examples also include previous research work at Daimler AG, Group Research and Advanced Engineering)

• Image-based 3D reconstruction methods for industrial inspection and metrology


3D reconstruction of a raw forged iron surface using a combination of shape from shading and fringe projection based active range scanning (lateral resolution is 0.042 mm per pixel) (from Herbort et al., ICIP 2011).


3D reconstruction of a raw forged iron surface based on a combined analysis of stereo, intensity, and polarisation features (top) and relying on monocular photopolarimetric features alone (bottom, a pixel corresponds to 0.30 mm) (from Wöhler and d'Angelo (2009), International Journal of Computer Vision 81; d'Angelo and Wöhler (2008), ISPRS Journal of Photogrammetry and Remote Sensing 63).

3D pose estimation of industrial parts (from von Bank et al., DAGM 2003).
• Methods for 3D object recognition, trajectory classification, and action recognition for robotics and advanced driver assistance systems


Recognition of working actions based on a non-stationary HMM framework. Top: Spatio-temporal 3D pose estimation of the hand-forearm limb with the Shape Flow algorithm. Bottom: Results of action recognition. Blue: transfer motion; red: screw_1; black: screw_2; green: clean; ochre: plug; white: unknown action; GT: ground truth (from Hahn et al., HCRS 2009).

Recognition of working actions and long-term motion prediction by classification of trajectories for human-robot interaction (from Hahn et al., ICVS 2008).

Spatio-temporal 3D pose estimation of the hand-forearm limb for human-robot interaction with the Shape Flow algorithm (from Hahn et al., ICPR 2008).

Spatio-temporal 3D pose estimation of the hand-forearm limb for human-robot interaction (from Barrois and Wöhler, ICVS 2008).


3D tracking of the hand-forearm limb and the head-shoulder contour for human-robot interaction (from Hahn et al., 3DIM 2007, Oldenburger 3D-Tage 2009).

Motion prediction system based on a low-dimensional manifold constructed in a high-dimensional feature space representing the vehicle trajectories using unsupervised kernel regression (Hermes et al., Dortmunder Auto-Tag 2011).

Recognition and prediction of a situation involving two vehicles turning left at a road intersection. The true future trajectories are denoted by dashed lines, while the solid lines correspond to the motion hypotheses of the vehicles (from Käfer et al., ICRA 2010).

Long-term trajectory prediction of vehicles based on multiple hypotheses (from Hermes et al., Oldenburger 3D-Tage 2009, IV 2009).

Segmentation and 3D pose estimation of vehicles based on stereo image analysis and optical flow estimation (from Barrois et al., IV 2009).
• Pattern recognition methods


Detection of US traffic signs using resource optimised cascaded perceptron classifiers (from Staudenmaier et al., CI 2010).

Real-world (left) and synthetically generated (right) training samples for traffic sign recognition (from Hoessler et al., ICVS 2007).
• Methods for image-based 3D reconstruction and analysis of multispectral data for remote sensing applications


Shaded DEM of high lateral resolution of the eastern part of the lunar crater Alphonsus (left) and of the lunar crater Menelaus (right), obtained based on a photometric approach using Chandrayaan-1 M3 imagery in combination with LOLA data (from Herbort et al., ICIP 2011 and Grumpe and Wöhler, ISPA 2011, respectively).

Result of automatic lunar crater detection using a DEM of high lateral resolution obtained based on a photometric approach using Chandrayaan-1 M3 imagery in combination with LOLA data. Green: previous LU60645GT catalogue; yellow: additionally detected craters (from Salamunićcar et al., ISPA 2011).

Global lunar petrographic map, displaying the relative abundance of the three most important lunar mineral types. Red channel: mare basalt; green channel: Mg-rich rock; blue channel: ferrous anorthosite (from Wöhler et al. (2011), Planetary and Space Science 59).

Left: DEM of the lunar volcanic dome Cauchy ω (from Wöhler et al. (2006), Icarus 183). Right: DEM of the northern half of the lunar crater Kepler, obtained based on a combined structure from motion and shape from shading analysis of a sequence of the Smart-1 AMIE camera (from d'Angelo and Wöhler (2008), ISPRS Journal of Photogrammetry and Remote Sensing 63).

For detailed information see list of publications.

Download of image sequences and ground truth data

A result of technology transfer: SafetyEYE

A research project for which my former colleague Dr. Lars Krüger and I were responsible at Daimler AG, Group Research and Advanced Engineering, has led to the development of the vision-based SafetyEYE system for three-dimensional surveillance of working areas in industrial production. This system has been created in cooperation between Daimler and the company Pilz GmbH & Co. KG, a specialist for safe automation.

SafetyEYE's trinocular camera sensor

General information about the functionality of SafetyEYE (cf. published press material):
The SafetyEYE system consists of three calibrated cameras which monitor the protection area around a machine, e. g. an industrial robot, and two high-performance industrial PCs. The implemented stereoscopic algorithms determine the three-dimensional structure of the scene being surveyed. As soon as a potentially hazardous situation is about to occur, the system initiates the protective measures necessary to prevent an accident, either by slowing down or by stopping the machine. An important advantage of the SafetyEYE system is the fact that it can be installed quickly and efficiently. While setting up a traditional safety system consisting of several components such as metal fences, light barriers, and laser scanners may take as long as one day, only a few hours are needed to configure SafetyEYE's three-dimensional protection areas. For the future, it is intended to increase the system capabilities towards a distinction between persons and objects. This will be a step towards collaborative working environments in which persons and machines are able to work simultaneously on the same workpiece.

SafetyEYE has received the Automation Award 2006 as the most outstanding product presented on the SPS Drives technology fair in Nürnberg 2006 along with further awards (GIT Sicherheit Award 2007, ISA Award 2007, Electrical Industry Awards 2007, ETOP Innovation Award). It is is among the five products nominated for the Hermes Award 2007 and has been nominated for the Deutscher Arbeitsschutzpreis.

Further details about the SafetyEYE system are given in the DaimlerChrysler Hightech Report 2/2006 and at eMercedesBenz). For further information, see the SafetyEYE product website featuring an illustrative video, and the website of the system supplier Pilz GmbH & Co. KG.

Further activities

• Member of the editorial board of the Springer journal 3D Research, editor of the topical issue "3D Computer Vision" (September 2010)
• Reviewer for the journals Pattern Recognition Letters, Image and Vision Computing, Earth and Planetary Science Letters, and Planetary and Space Science
• Co-organiser of the special session "Image Processing and Analysis in Lunar and Planetary Science" of the 7th International Symposium on Image and Signal Processing and Analysis (ISPA 2011), Dubrovnik, Croatia, September 4-6, 2011
• Member of the programme committee of the International Conference on Computer Vision Theory and Applications (VISAPP) 2011 and 2012
• Co-organiser of the symposium Oldenburger 3D-Tage 2009
• Member of the programme committee of the 2nd Workshop Robot Vision 2008, Auckland, New Zealand, February 18-20, 2008
• Member of the programme committee of the International Conference on Computer Vision Systems (ICVS) 2008, Santorini, Greece, May 12-14, 2008
• Member of the programme committee, exhibition and industrial relations chair of the International Conference on Computer Vision Systems (ICVS) 2007, Bielefeld, Germany, March 21-24, 2007
• Organisation of the workshop "Camera Calibration Methods for Computer Vision Systems (CCMVS 2007)" in conjunction with ICVS 2007

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