Aktuelle Publikationen

Optical feedback has an impact on the tunability of lasers. We created a model of a tunable distributed Bragg reflector (DBR) laser describing the effect of optical feedback from a constant reflector distance on the wavelength tuning. Theoretical and experimental results are in good agreement. A further discussion of the model sheds light on design rules to reduce the effect of optical feedback on the tuning behavior. We introduced a new parameter called mode loss difference (MLD) as a metric for the feedback tolerance of the tuning behavior. A large MLD indicates higher tolerance of the laser to cavity length variations.

A morphed face image is a fusion of two face images and represents biometrics of two different subjects. Embedded in an official document, it can cause immense damage, since both subjects can claim its ownership and thus share an identity. In this paper, we propose and compare different neural network training schemes based on alternations of training data to obtain accurate and robust detectors for such kind of fraud. In addition, we use layer-wise relevance propagation (LRP) to analyze the differently trained networks in depth.

Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. However, FL yields suboptimal results if the local clients’ data distributions diverge. The proposed Clustered FL approach tackles the problem by identifying diverging clients and grouping them into separate clusters.

We present an end-to-end pipeline for the creation of high-quality animatable volumetric video content of human performances. Going beyond the application of free-viewpoint volumetric video, we allow re-animation of an actor’s performance through (i) the enrichment of the captured data with semantics and animation properties and (ii) hybrid geometry- and video-based animation combined with neural infilling.

Coherent comb lasers may serve as a source for multiwavelength modulators in short reach transmission, or for phase controlled OFDM channels in long reach. We explore and compare quantum dot (QD) and quantum well (QW) lasers with more than 33 channels in the DWDM 50 GHz grid, thus enabling > 1 Tb/s optical transmission. In addition, the mode-locked devices can be applied as pulse sources with < 500 fs pulses by using a simple SMF.

Photo-realistic modeling and rendering of humans is extremely important for VR environments. While purely computer graphics modeling can achieve highly realistic human models, achieving real photo-realism with these models is computationally extremely expensive. Therefore, we enrich volumetric video with semantics and animation properties to make photo-realistic volumetric video animatable.

We determine the absorption and scattering coefficients of cholesteatoma and bone. In the near-UV and visual spectrum, clear differences exist between both tissues. These differences reveal the future possibility to detect and identify, automatically or semi-automatically, cholesteatoma tissue for active treatment decisions during image-guided surgery leading to a better surgical outcome.

This paper presents DeepCABAC, a universal compression algorithm for deep neural networks (DNNs) that through its adaptive, context-based rate modeling, allows an optimal quantization and coding of parameters. It compresses DNNs up to 5% of their original size with no accuracy loss and has been selected as basic compression technology for the emerging MPEG-7 part 17 standard on DNN compression.

We enable dynamic projection mapping on 3d objects and present a model-based tracking strategy for projector camera-systems, which directly takes advantage from the projected content for pose estimation. Our method establishes a distortion free projection by first analysing and then correcting the distortion of the projection in a closed loop. Therefor an optical flow-based model is extended to the geometry of a projector-camera unit. We evaluate our procedure with real and synthetic images and obtain very precise registration results.

We analyze five healthy soft tissue types as well as tumor tissue in terms of absorption and scatter coefficients in the spectral range of 250 to 800 nm. The spectral properties of the analyzed tissue types show relevant differences in this specific spectral range. This knowledge can be used for different medical and biomedical application areas as computer-aided diagnostics, intraoperative image guidance and histological analysis.

This paper introduces the current running project MultiARC, which has the aim to combine stereoscopic measurements and scene reconstruction with hyperspectral tissue differentiation in a surgical microscope and 3D endoscope for image-guided surgery.

In this paper, we present an approach for creating animatable 3D models from temporal monocular image acquisitions of non-rigid objects. During deformation, the object of interest is captured with only a single camera under full perspective projection. The aim of the presented framework is to obtain a shape deformation model in terms of joints and skinning weights that can finally be used for animating the model vertices.

The recent trend towards miniaturization of mobile projectors is allowing new forms of information presentation and interaction. Projectors can easily be moved freely in space either by humans or by mobile robots. This paper presents a technique to dynamically track the orientation and position of the projection plane only by analyzing the distortion of the projection by itself, independent of the presented content. It allows distortion-free projection with a fixed metric size for moving projector-camera systems.

The task of detecting and classifying damages in sewer pipes offers an important application area for computer vision algorithms. This paper describes a system, which is capable of accomplishing this task solely based on low quality and severely compressed fisheye images from a pipe inspection robot. Relying on robust image features, we estimate camera poses, model the image lighting, and exploit this information to generate high quality cylindrical unwraps of the pipes' surfaces. Based on the generated images, we apply semantic labeling based on deep convolutional neural networks to detect and classify defects as well as structural elements.