63 citations · 196 across the 35 of their papers we have counts for
7 papers · 1 filter
Improving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes
Florian Fervers, Timo Breuer, Gregor Stachowiak +3
Models for semantic segmentation require a large amount of hand-labeled training data which is costly and time-consuming to produce. For this purpose, we present a label fusion fra…
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…
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction
Stefan Becker, Ronny Hug, Wolfgang Hübner +2
In applications such as object tracking, time-series data inevitably carry missing observations. Following the success of deep learning-based models for various sequence learning t…
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…