Impact on Business

Boutros, Fadi; Damer, Naser; Raja, Kiran; Ramachandra, Raghavendra; Kirchbuchner, Florian; Kuijper, Arjan

Iris and Periocular Biometrics for Head Mounted Displays: Segmentation, Recognition, and Synthetic Data Generation.

Impact on Science

Mahajan, Shweta; Gurevych, Iryna; Roth, Stefan

Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings.

Impact on Society

Schufrin, Marija; Reynolds, Steven Lamarr; Kuijper, Arjan; Kohlhammer, Jörn

Visualization Interface to Improve the Transparency of Collected Personal Data on the Internet.

Liste der Publikationen

Gewinner und die Publikationen, die in die engere Wahl gekommen sind.
Show publication details

Schufrin, Marija; Reynolds, Steven Lamarr; Kuijper, Arjan; Kohlhammer, Jörn

A Visualization Interface to Improve the Transparency of Collected Personal Data on the Internet

2021

IEEE Transactions on Visualization and Computer Graphics

Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents TransparencyVis - a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources.

Show publication details

Baumgartl, Tom; Petzold, Markus; Wunderlich, Marcel; Höhn, Markus; Archambault, Daniel; Lieser, M.; Dalpke, Alexander; Scheithauer, Simone; Marschollek, Michael; Eichel, V. M.; Mutters, Nico T.; Landesberger, Tatiana von

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

2021

IEEE Transactions on Visualization and Computer Graphics

Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak – the patient zero or index patient – requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.

Show publication details

Kügler, David; Uecker, Marc; Kuijper, Arjan; Mukhopadhyay, Anirban

AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation

2020

Medical Image Computing and Computer Assisted Intervention - MICCAI 2020

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <23, 2020, Online>

Lecture Notes in Computer Science (LNCS), 12263

Despite recent successes, the advances in Deep Learning have not yet been fully translated to Computer Assisted Intervention (CAI) problems such as pose estimation of surgical instruments. Currently, neural architectures for classification and segmentation tasks are adopted ignoring significant discrepancies between CAI and these tasks. We propose an automatic framework (AutoSNAP) for instrument pose estimation problems, which discovers and learns architectures for neural networks. We introduce 1) an efficient testing environment for pose estimation, 2) a powerful architecture representation based on novel Symbolic Neural Architecture Patterns (SNAPs), and 3) an optimization of the architecture using an efficient search scheme. Using AutoSNAP, we discover an improved architecture (SNAPNet) which outperforms both the hand-engineered i3PosNet and the state-of-the-art architecture search method DARTS.

Show publication details

Dong, Jiangxin; Roth, Stefan; Schiele, Bernt

Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring

2020

Advances in Neural Information Processing Systems

Annual Conference on Neural Information Processing Systems (NeurIPS) <34, 2020, Online>

We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with both simulated and real-world image blur. Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts. Moreover, our approach quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin.

Show publication details

Terhörst, Philipp; Kolf, Jan Niklas; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition

2020

IJCB 2020. IEEE/IARP International Joint Conference on Biometrics

IEEE/IARP International Joint Conference on Biometrics (IJCB) <2020, online>

Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embed-dings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.

Show publication details

Boutros, Fadi; Damer, Naser; Raja, Kiran; Ramachandra, Raghavendra; Kirchbuchner, Florian; Kuijper, Arjan

Iris and Periocular Biometrics for Head Mounted Displays: Segmentation, Recognition, and Synthetic Data Generation

2020

Image and Vision Computing

Augmented and virtual reality deployment is finding increasing use in novel applications. Some of these emerging and foreseen applications allow the users to access sensitive information and functionalities. Head Mounted Displays (HMD) are used to enable such applications and they typically include eye facing cameras to facilitate advanced user interaction. Such integrated cameras capture iris and partial periocular region during the interaction. This work investigates the possibility of using the captured ocular images from integrated cameras from HMD devices for biometric verification, taking into account the expected limited computational power of such devices. Such an approach can allow user to be verified in a manner that does not require any special and explicit user action. In addition to our comprehensive analyses, we present a light weight, yet accurate, segmentation solution for the ocular region captured from HMD devices. Further, we benchmark a number of well-established iris and periocular verification methods along with an in-depth analysis on the impact of iris sample selection and its effect on iris recognition performance for HMD devices. To the end, we also propose and validate an identity-preserving synthetic ocular image generation mechanism that can be used for large scale data generation for training purposes or attack generation purposes. We establish the realistic image quality of generated images with high fidelity and identity preserving capabilities through benchmarking them for iris and periocular verification.

Show publication details

Mahajan, Shweta; Gurevych, Iryna; Roth, Stefan

Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings

2020

Eighth International Conference on Learning Representations

International Conference on Learning Representations (ICLR) <8, 2020, Online>

Learned joint representations of images and text form the backbone of several important cross-domain tasks such as image captioning. Prior work mostly maps both domains into a common latent representation in a purely supervised fashion. This is rather restrictive, however, as the two domains follow distinct generative processes. Therefore, we propose a novel semi-supervised framework, which models shared information between domains and domain-specific information separately. The information shared between the domains is aligned with an invertible neural network. Our model integrates normalizing flow-based priors for the domain-specific information, which allows us to learn diverse many-to-many mappings between the two domains. We demonstrate the effectiveness of our model on diverse tasks, including image captioning and text-to-image synthesis.

Show publication details

Ströter, Daniel; Mueller-Roemer, Johannes; Stork, André; Fellner, Dieter W.

OLBVH: Octree Linear Bounding Volume Hierarchy for Volumetric Meshes

2020

The Visual Computer

We present a novel bounding volume hierarchy for GPU-accelerated direct volume rendering (DVR) as well as volumetric mesh slicing and inside-outside intersection testing. Our novel octree-based data structure is laid out linearly in memory using space filling Morton curves. As our new data structure results in tightly fitting bounding volumes, boundary markers can be associated with nodes in the hierarchy. These markers can be used to speed up all three use cases that we examine. In addition, our data structure is memory-efficient, reducing memory consumption by up to 75%. Tree depth and memory consumption can be controlled using a parameterized heuristic during construction. This allows for significantly shorter construction times compared to the state of the art. For GPU-accelerated DVR, we achieve performance gain of 8.4×–13×. For 3D printing, we present an efficient conservative slicing method that results in a 3×–25× speedup when using our data structure. Furthermore, we improve volumetric mesh intersection testing speed by 5×–52×.

Show publication details

Terhörst, Philipp; Kolf, Jan Niklas; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness

2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) <2020, online>

Face image quality is an important factor to enable high-performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for the purpose of recognition. Previous work proposed supervised solutions that require artificially or human labelled quality values. However, both labelling mechanisms are error prone as they do not rely on a clear definition of quality and may not know the best characteristics for the utilized face recognition system. Avoiding the use of inaccurate quality labels, we proposed a novel concept to measure face quality based on an arbitrary face recognition model. By determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated. The experiments are conducted in a cross-database evaluation setting on three publicly available databases. We compare our proposed solution on two face embeddings against six state-of-the-art approaches from academia and industry. The results show that our unsupervised solution outperforms all other approaches in the majority of the investigated scenarios. In contrast to previous works, the proposed solution shows a stable performance over all scenarios. Utilizing the deployed face recognition model for our face quality assessment methodology avoids the training phase completely and further outperforms all baseline approaches by a large margin. Our solution can be easily integrated into current face recognition systems, and can be modified to other tasks beyond face recognition.