The computer vision group deals with general problems of detecting structures in images. Particular topics currently include color & reflectance, image forensics, multispectral imaging, multi-camera setups and range imaging. Our work is closely related to other main fields in computer vision, like image segmentation and tracking. Particular topics like image forensics connect closely to statistics, color & reflectance serves often as a pre-processing step for higher level computer vision tasks like object recognition.
The pathologist is still the gold standard in the diagnosis of diseases in tissue slides. Due to its human nature, the pathologist is on one side able to flexibly adapt to the high morphological and technical variability of histologic slides but of limited objectivity due to cognitive and visual traps.
In diverse project we are applying and validating currently available tools and solutions in digital pathology but are also developing new solution in automated image analysis to complement and improve the pathologist especially in areas of quantitative image analysis.
The goal of this project is the investigation of multimodal methods for the evaluation of interventional workflows in the operation room. This topic will be researched in an international project context with partners in Germany and in Brazil (UNISINOS in Porto Alegre). Methods will be developed to analyze the processes in an OR based on signals from body-worn sensors, cameras and other modalities like X-ray images recorded during the surgeries. For data analysis, techniques from the field of…
The project deals with an important aspect of art, archeology and cultural heritage: iconography and narrative
imagery. After using Computer Vision to largely recognize objects and styles, the next challenging step is to understand the semantic level of the images. The goal is the interaction between scientists and machine, not only in the form of applied science, but as a transdisciplinary exchange of methods and image theories.
The goal of this project is to detect cancerous tissue in confocal lasermicroendoscopy (CLE) images of the oral cavity and the vocal cord. The current treatment of these diseases is a histological analysis of specimen and a surgical resection, which has a rather high long-term survival rate, or radiation therapy with a lower survival rate. An early detection of cancerous tissue could lead to a lowered complication rate for further treatment, as well as a better overall prognosis for patients. Further, an in-vivo diagnosis during operation could narrow down the area for the necessary surgical excision, which is especially beneficial for cancer of the vocal cords.
For this reason, we are applying methods of pattern recognition to facilitate and support diagnosis. We were able to show that these can be applied with high accuracies on CLE images.
The analysis of the similarity of portraits is an important issue for many sciences such as art history or digital humanities, as for instance it might give hints concerning serial production processes, authenticity or temporal and contextual classification of the artworks.
In the project, first algorithms will be developed for cross-genre and multi-modal registration of portraits to overlay digitized paintings and prints as well as paintings acquired with different imaging systems such as…
- Sindel A., Breininger K., Käßer J., Heß A., Maier A., Köhler T.:
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
25th IEEE International Conference on Image Processing, ICIP 2018
- Buerhop-Lutz C., Hoffmann M., Reeb L., Pickel T., Hauch J., Maier A.:
Applying Deep Learning Algorithms to EL-images for Predicting the Module Power
36th European Photovoltaic Solar Energy Conference and Exhibition (Marseille, September 9, 2019 - September 13, 2019)
In: Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition 2019
- Madhu P., Kosti RV., Mührenberg L., Bell P., Maier A., Christlein V.:
Recognizing Characters in Art History Using Deep Learning
SUMAC 2019 - The 1st workshop on Structuring and Understanding of Multimedia heritAge Contents (Nice, October 21, 2019 - October 25, 2019)
In: Recognizing Characters in Art History Using Deep Learning 2019
- Bertram CA., Aubreville M., Marzahl C., Maier A., Klopfleisch R.:
A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor
In: Scientific Data 6 (2019), p. 1-9
- Lenc L., Martínek J., Král P., Nicolao A., Christlein V.:
HDPA: historical document processing and analysis framework
In: Evolving Systems (2020)