Recent publications

January 2020

UDSMProt: Universal Deep Sequence Models for Protein Classification

Nils Strodthoff, Wojciech Samek, Patrick Wagner, Markus Wenzel

Inferring the properties of protein from its amino acid sequence is a key problem in bioinformatics. We put forward UDSMProt, a universal deep sequence model that is pretrained on a language modeling task and finetuned on protein classification tasks. For enzyme class prediction, gene ontology prediction and remote homology and fold detection, we reach sofa performance from the sequence alone.


August 2019

Projection Distortion-based Object Tracking in Shader Lamp Scenarios

Niklas Gard, Peter Eisert, Anna Hilsmann

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.


July 2019

Determination of Optical Properties of Human Tissues obtained from Parotidectomy in the Spectral Range of 250 to 800 nm

Eric Wisotzky, Peter Eisert, Anna Hilsmann, Florian Uecker, Steffen Dommerich

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.


March 2019

Interactive and Multimodal-based Augmented Reality for Remote Assistance using a Digital Surgical Microscope

Eric Wisotzky, Peter Eisert, Anna Hilsmann, Jean-Claude Rosenthal, Florian Uecker, Armin Schneider, Falko Schmid, Michael Bauer

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.


October 2018

Animatable 3D Model Generation from 2D Monocular Visual Data

Philipp Fechteler, Peter Eisert, Anna Hilsmann, Lisa Kausch

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.


October 2018

Markerless Closed-Loop Projection Plane Tracking for Mobile Projector-Camera Systems

Niklas Gard, Peter Eisert

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.


March 2018

Automatic Analysis of Sewer Pipes Based on Unrolled Monocular Fisheye Images

Johannes Wolf Künzel, Peter Eisert, Thomas Werner, Ronja Möller, Jan Waschnewski, Ralf Hilpert

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.



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