Petr Dokládal is a senior researcher with the Center for Mathematical Morphology, a joint research lab of Armines and MINES ParisTech, Paris, France. He graduated from the Technical University in Brno, Czech Republic, in 1994, as a telecommunication engineer, received his Ph.D. degree in 2000 from the Marne la Vallée University, France, in general computer sciences, specialized in image processing and received his habilitation from the ParisEst University in 2013. His research interests include mathematical morphology, image segmentation, object tracking and pattern recognition.
Image processing in Non-Destructive Testing
Non-destructive testing is a frequent task in industry for material control and structure inspection. There are many imaging techniques available to make defects visible. Effort is being made to automatize the process to make it repeatable, more accurate, cheaper and environment friendly. Others techniques (able to work remotely, easier to automatize) are being developed. Most of these techniques are still followed by a visual inspection performed by a qualified personnel.
In the beginning of this talk we will review a few, various inspection techniques used in industry. In the second part we will focus on the detection of cracks. From the image processing angle of view cracks are thin, curvilinear structures. They are not always easy to detect especially when surrounded by noise. We show in this talk how cracks can be detected by using path openings, an operator from mathematical morphology. Then, inspired by the a contrario approach, we will show how to choose a convenient threshold value to obtain a binary result. The a contrario approach, instead of modeling the structures to detect, models the noise to detect structures deviating from the model. In this scope, we assume noise composed of pixels that are independent random variables. Henceforth, cracks that are curvilinear and not necessarily connected sequences of bright pixels, are detected as abnormal sequences of bright pixels. In the second part, a fast approximation of the solution based on parsimonious path openings is shown.
His talk takes place on Tuesday, September 18, 2018 at 13:00 in room A113.
Torsten Sattler received a PhD in Computer Science from RWTH Aachen University, Germany, in 2014 under the supervision of Prof. Bastian Leibe and Prof. Leif Kobbelt. In December 2013, he joined the Computer Vision and Geometry Group of Prof. Marc Pollefeys at ETH Zurich, Switzerland, where he currently is a senior researcher and Marc Pollefeys’ deputy while Prof. Pollefeys is on leave from ETH. His research interests include (large-scale) image-based localization using Structure-from-Motion point clouds, real-time localization and SLAM on mobile devices and for robotics, 3D mapping, Augmented abd Virtual Reality, machine learning, (multi-view) stereo, image retrieval and efficient spatial verification, camera calibration and pose estimation. His current work focuses on making algorithms for localization and mapping “smarter” by incorporating higher-level scene understanding.
Torsten has worked on dense sensing for self-driving cars as part of the V-Charge project. He is currently involved in enabling semantic SLAM and re-localization for gardening robots (as part of the Trimbot2020 project, a EU Horizon 2020 project where he leads the efforts on a workpackage), research for Google’s Tango project, where he leads CVG’s research efforts, and in work on self-driving cars.
Torsten has organized multiple tutorials and workshops at CVPR and ICCV. He regularly serves as a reviewer for the top-conferences in Computer Vision (CVPR, ECCV, ICCV) and Robotics (IROS, ICRA) and is an area chair for CVPR 2018 and 3DV 2018.
His talk takes place in May / June 2018.
Miloslav Druckmüller is a Professor of Applied Mathematics at the Institute of mathematics, Faculty of Mechanical Engineering, Brno University of Technology and the head of the Department of Computer Graphics and Geometry. His main interests are numerical methods of image analysis, digital image processing, computer graphics and complex variable analysis. During the last 10 years he has been cooperating widely with the Institute for Astronomy, University of Hawaii in the field of solar coronal plasma research. He created a large archive of K-corona (photospheric light scattered on free electrons) images and temperature maps based on Fe and Ni ions observing based on data obtained during total solar eclipses during last two decades. Nowadays his research is mainly focused on processing and analysis of data obtained by NASA SDO spacecraft. His talk takes place in POSTPONED.
Kevin Köser is a senior researcher at the GEOMAR Helmholtz Centre for Ocean Research, Kiel. His main research interest lies in novel camera-based measurement techniques for (deep) sea environments and processes (3D underwater vision). These help to study resources, to explore and monitor (deep) sea habitats or to assess hazards, e.g. with respect to gas flux or seafloor dynamics. In the past years Dr. Köser has taught the classes 3D Photography and Computer Vision Lab at the Swiss Federal Institute of Technology (ETH Zurich) and has worked as a senior researcher in ETH’s Computer Vision and Geometry Lab on shape and motion extraction from photos and videos, geolocalization and image registration. POSTPONED