1 code implementation • 6 Oct 2020 • Christoph Heindl, Lukas Brunner, Sebastian Zambal, Josef Scharinger
Solving complex computer vision tasks by deep learning techniques relies on large amounts of (supervised) image data, typically unavailable in industrial environments.
no code implementations • 22 Jun 2020 • Christoph Heindl
The success of deep learning has revolutionized many fields of research including areas of computer vision, text and speech processing.
no code implementations • 11 Oct 2019 • Sebastian Zambal, Christoph Heindl, Christian Eitzinger, Josef Scharinger
This leads to an appealing method that scales well with new defect types and measurement devices and requires little real world data for training.
no code implementations • 8 Oct 2019 • Christoph Heindl, Markus Ikeda, Gernot Stübl, Andreas Pichler, Josef Scharinger
The rapid growth of collaborative robotics in production requires new automation technologies that take human and machine equally into account.
1 code implementation • 6 Oct 2019 • Christoph Heindl, Markus Ikeda, Gernot Stübl, Andreas Pichler, Josef Scharinger
The raise of collaborative robotics has led to wide range of sensor technologies to detect human-machine interactions: at short distances, proximity sensors detect nontactile gestures virtually occlusion-free, while at medium distances, active depth sensors are frequently used to infer human intentions.
1 code implementation • 3 Jul 2019 • Christoph Heindl, Sebastian Zambal, Josef Scharinger
This work considers robot keypoint estimation on color images as a supervised machine learning task.
1 code implementation • 1 Jul 2019 • Christoph Heindl, Thomas Pönitz, Gernot Stübl, Andreas Pichler, Josef Scharinger
Commodity RGB-D sensors capture color images along with dense pixel-wise depth information in real-time.
no code implementations • 1 Jul 2019 • Christoph Heindl, Thomas Pönitz, Andreas Pichler, Josef Scharinger
We propose a novel 3D human pose detector using two panoramic cameras.
1 code implementation • 13 Feb 2019 • Christoph Heindl, Sebastian Zambal, Thomas Ponitz, Andreas Pichler, Josef Scharinger
This paper considers the task of locating articulated poses of multiple robots in images.