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Research Groups

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.

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The Image Analysis Group is dedicated to extract information from images. Examples are the outlining of specific structures in 2D and 3D images, like extraction of pages in CT scans of books or the detection of lesions in mammographic images.

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In close collaboration with leading clinical and industrial partners, the Image Fusion (IMF) Group develops novel methods for rigid and non-rigid data registration, as well as innovative applications and efficient clinical workflows. Current foci of interest include multi-modal image fusion, image-guided therapy, 2-D/3-D registration and image overlay. The interdisciplinary research provides the basis to develop applications close to medical practice.

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In general, inverse problems are concerned with (1) reconstructing signals from observations and/or (2) controlling systems to a desired effect. Inverse problems occur in a variety of domains and applications, ranging from tomographic reconstruction to particle physics to machine learning.  The IPA group is dedicated to identifying and solving such inverse problems with a strong focus on radiological applications:
•    Tomographic reconstruction for different modalities under non-optimal conditions
•    Rigid and non-rigid motion estimation and correction
•    Image quality
•    Blood flow analysis
•    Ionizing radiation dose estimation and optimization

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The big data analytics group is concerned with combining multi-modal imaging and clinical data for improved clinical decision making. Current topics of interest include identification of a malignant tumor sub-types in breast cancer, establishing correlations between image-based features, gene expression and disease progression in patients, and developing innovative therapeutic approaches such as immune cell guidance and response activation.  

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The magnetic resonance imaging group is dedicated to excellent research in MR reconstruction. An overview on the running projects can be found on this website.

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In the recent decade, ophthalmic imaging has proven to be a steadily growing field of research. A milestone in ophthalmology was the transfer of Optical Coherence Tomography (OCT) from research to clinical use. Compared to conventional fundus photography, OCT allows the three dimensional, depth resolved visualization of the human retina, while preserving the non-invasiveness. Recently, a radical change occured in the field of OCT research with the clinical introduction of OCT angiography (OCTA), which further adds dye-free imaging of the underlying vasculature. Research Focus To facilitate an efficient analysis of this vastly increased amount of information, new processing algorithms are required to support the treating clinician. Our research focus is twofold: On the one hand, we develop advanced motion compensation, shadow artifact compensation and signal reconstruction algorithms to achieve artifact-free OCT(A) signals of best possible quality. On the other hand, we aid accurate image analysis by improving layer and vessel segmentations, categorizing vessels into arteries/veins or pathology. By combining both efforts, we want to improve the understanding of the most prevalent eye diseases, allowing for more accurate treatment and thus improved patient outcome. Multidisciplinary Collaborations
To enable research with state-of-the-art technology while preserving a close link to the clinical needs, the work of our group is embedded in a multidisciplinary environment including optical engineers at the Massachusetts Institute of Technology, Cambridge, USA and clinicians at the New England Eye Center, Boston, USA and the Department of Ophthalmology at the University Clinic Erlangen.

Advanced shadow artifact removal reveals the unique structures of the superficial vascular plexus (SVP), the intermediate capillary plexus (ICP) and the deep capillary plexus (DCP)

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Precision Learning is a research direction, seeking to integrate known operators into machine learning models to improve generalization und efficiency. Known operators have been shown to hold the potential of reducing maximal error bounds when incorporated into deep neural networks. This suggests their inclusion could allow models to learn from less data and increase robustness.

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Apart from automatic feature extraction and subsequent speech recognition, our chair deals with the following topics: spoken dialogue systems, recognition and processing of unknown, so-called out-of-vocabulary words, automatic analysis and classification of prosodic phenomena such as accent and phrase boundaries. Another core topic is the automatic recognition of emotion-related, affective user states based on acoustic and linguistic features; moreover, we use multi-modal information for this task, including an analysis of facial expressions, gestures, and physiological parameters. Another topic is the multi-modal recognition of the user's focus of attention in human-machine-interaction. Finally, we work on the analysis of pathologic speech such as speech from children with cleft lip and palate or patients after laryngectomy (removal of the larynx after cancer).

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Early detection of cancer and angiography applications are just two examples that can directly benefit from an imaging modality with excellent soft-tissue contrast. X-ray grating interferometry is promising to achieve this while keeping radiation dose and examination costs low. Existing clinical X-ray systems can be retrofitted with a set of three gratings to form an interferometer. The measurement procedure yields three images: X-ray absorption, differential phase, and dark-field. The vision of phase-contrast X-ray is that these three complementary signals together enable highly sensitive tissue contrast for medical diagnosis and interventional applications. The X-ray phase contrast group at the Pattern Recognition Lab develops algorithms for processing these images, and for the 3-D reconstruction of tomographic acquisitions. We closely collaborate with the physicist at ECAP, who are maintaining the experimental setup.

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Colloquia timetable

For a summary of scheduled colloquia, please refer to the colloquia timetable.