52 citations · 85 across the 13 of their papers we have counts for
23 papers
A Comparison of Deep Saliency Map Generators on Multispectral Data in Object Detection
Jens Bayer, David Münch, Michael Arens
Deep neural networks, especially convolutional deep neural networks, are state-of-the-art methods to classify, segment or even generate images, movies, or sounds. However, these me…
Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction
Stefan Becker, Ronny Hug, Wolfgang Hübner +2
Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are incr…
MODISSA: a multipurpose platform for the prototypical realization of vehicle-related applications using optical sensors
Björn Borgmann, Volker Schatz, Marcus Hammer +3
We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and…
3D Surface Reconstruction From Multi-Date Satellite Images
Sebastian Bullinger, Christoph Bodensteiner, Michael Arens
The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable pro…
Handling Missing Observations with an RNN-based Prediction-Update Cycle
Stefan Becker, Ronny Hug, Wolfgang Hübner +2
In tasks such as tracking, time-series data inevitably carry missing observations. While traditional tracking approaches can handle missing observations, recurrent neural networks…
A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Tracking
Sebastian Bullinger, Christoph Bodensteiner, Michael Arens
We propose a framework that extends Blender to exploit Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques for image-based modeling tasks such as sculpting or camera…