Publications

Simplified Concrete Dropout - Improving the Generation of Attribution Masks for Fine-grained Classification
Dimitri Korsch and Maha Shadaydeh and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). 2023.
[bibtex] [pdf] [code] [supplementary] [abstract]

Automated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps
Dimitri Korsch and Paul Bodesheim and Gunnar Brehm and Joachim Denzler.
CVPR Workshop on Fine-grained Visual Classification (CVPR-WS). 2022.
[bibtex] [pdf] [web] [code] [abstract]

Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification
Dimitri Korsch and Paul Bodesheim and Joachim Denzler.
INFORMATIK 2021, Computer Science for Biodiversity Workshop (CS4Biodiversity). Pages 443-460. 2021.
[bibtex] [pdf] [web] [code] [abstract]

Exploiting Web Images for Moth Species Classification
Julia Böhlke and Dimitri Korsch and Paul Bodesheim and Joachim Denzler.
Computer Science for Biodiversity Workshop (CS4Biodiversity), INFORMATIK 2021. Pages 481-498. 2021.
[bibtex] [pdf] [web] [abstract]

Lightweight Filtering of Noisy Web Data: Augmenting Fine-grained Datasets with Selected Internet Images
Julia Böhlke and Dimitri Korsch and Paul Bodesheim and Joachim Denzler.
International Conference on Computer Vision Theory and Applications (VISAPP). Pages 466-477. 2021.
[bibtex] [pdf] [web] [abstract]

Automated Visual Large Scale Monitoring of Faunal Biodiversity
Bernd Radig and Paul Bodesheim and Dimitri Korsch and Joachim Denzler and Timm Haucke and Morris Klasen and Volker Steinhage.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 31 (3): pages 477-488. 2021.
[bibtex] [pdf] [web] [abstract]

Abstract: To observe biodiversity, the variety of plant and animal life in the world or in a particular habitat, human observers make the most common examinations, often assisted by technical equipment. Measuring objectively the number of different species of animals, plants, fungi, and microbes that make up the ecosystem can be difficult. In order to monitor changes in biodiversity, data have to be compared across space and time. Cameras are an essential sensor to determine the species range, abundance, and behavior of animals. The millions of recordings from camera traps set up in natural environments can no longer be analyzed by biologists. We started research on doing this analysis automatically without human interaction. The focus of our present sensor is on image capture of wildlife and moths. Special hardware elements for the detection of different species are designed, implemented, tested, and improved, as well as the algorithms for classification and counting of samples from images and image sequences, e.g., to calculate presence, absence, and abundance values or the duration of characteristic activities related to the spatial mobilities. For this purpose, we are developing stereo camera traps that allow spatial reconstruction of the observed animals. This allows three-dimensional coordinates to be recorded and the shape to be characterized. With this additional feature data, species identification and movement detection are facilitated. To classify and count moths, they are attracted to an illuminated screen, which is then photographed at intervals by a high-resolution color camera. To greatly reduce the volume of data, redundant elements and elements that are consistent from image to image are eliminated. All design decisions take into account that at remote sites and in fully autonomous operation, power supply on the one hand and possibilities for data exchange with central servers on the other hand are limited. Installation at hard-to-reach locations requires a sophisticated and demanding system design with an optimal balance between power requirements, bandwidth for data transmission, required service and operation in all environmental conditions for at least ten years.

End-to-end Learning of Fisher Vector Encodings for Part Features in Fine-grained Recognition
Dimitri Korsch and Paul Bodesheim and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 142-158. 2021.
[bibtex] [pdf] [web] [code] [abstract]

Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Dimitri Korsch and Paul Bodesheim and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 62-75. 2019.
[bibtex] [pdf] [web] [code] [abstract]

In Defense of Active Part Selection for Fine-Grained Classification
Dimitri Korsch and Joachim Denzler.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 28 (4): pages 658-663. 2018.
[bibtex] [pdf] [web] [abstract]

Finding Demand for Products in the Social Web
Philipp Berger and Patrick Hennig and Stefan Bunk and Dimitri Korsch and Daniel Kurzynski and Christoph Meinel.
IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity). Pages 432-439. 2015.
[bibtex]

Implementation of a Secure and Reliable Storage Above the Untrusted Clouds
Maxim Schnjakin and Dimitri Korsch and Martin Schoenberg and Christoph Meinel.
International Conference on Computer Science \& Education (ICCSE). Pages 347-353. 2013.
[bibtex]