8 citations · 13 across the 3 of their papers we have counts for
7 papers
Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning
Jakob Greis, Artem Yushchenko, Daniel Vogel +2
Many IoT devices are vulnerable to attacks due to flawed security designs and lacking mechanisms for firmware updates or patches to eliminate the security vulnerabilities. Device-t…
Exploiting Depth Information for Wildlife Monitoring
Timm Haucke, Volker Steinhage
Camera traps are a proven tool in biology and specifically biodiversity research. However, camera traps including depth estimation are not widely deployed, despite providing valuab…
Image-based Automated Species Identification: Can Virtual Data Augmentation Overcome Problems of Insufficient Sampling?
Morris Klasen, Dirk Ahrens, Jonas Eberle +1
Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infr…
An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks
Jonatan Grimm, Katja Herzog, Florian Rist +3
In (grapevine) breeding programs and research, periodic phenotyping and multi-year monitoring of different grapevine traits, like growth or yield, is needed especially in the field…
Efficient identification, localization and quantification of grapevine inflorescences in unprepared field images using Fully Convolutional Networks
Robert Rudolph, Katja Herzog, Reinhard Töpfer +1
Yield and its prediction is one of the most important tasks in grapevine breeding purposes and vineyard management. Commonly, this trait is estimated manually right before harvest…
Automated Phenotyping of Epicuticular Waxes of Grapevine Berries Using Light Separation and Convolutional Neural Networks
Pierre Barré, Katja Herzog, Rebecca Höfle +3
In viticulture the epicuticular wax as the outer layer of the berry skin is known as trait which is correlated to resilience towards Botrytis bunch rot. Traditionally this trait is…