Impact on Business

Le Moan, Steven; Tanksale, Tejas Madan; Byshko, Roman; Urban, Philipp

An Observer-Metamerism Sensitivity Index for Electronic Displays

Impact on Society

Papaioannou, Georgios; Schreck, Tobias; Andreadis, Anthousis; Mavridis, Pavlos; Gregor, Robert; Sipiran, Ivan; Vardis, Konstantinos

From Reassembly to Object Completion: A Complete Systems Pipeline

Impact on Science

Hur, Junhwa; Roth, Stefan

MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation

Liste der Publikationen

Gewinner und die Publikationen, die in die engere Wahl gekommen sind.
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Oyarzun Laura, Cristina; Drechsler, Klaus; Wesarg, Stefan; Bale, Reto

Accurate Physics-Based Registration for the Outcome Validation of Minimal Invasive Interventions and Open Liver Surgeries

2017

IEEE Transactions on Biomedical Engineering

The purpose of this paper is to present an outcome validation tool for tumor radiofrequency (RF) ablation and resection. Methods: Intervention assessment tools require an accurate registration of both pre- and postoperative computed tomographies able to handle big deformations. Therefore, a physics-based method is proposed with that purpose. To increase the accuracy both automatically detected internal and surface physical landmarks are incorporated in the registration process. Results: The algorithm has been evaluated in 25 clinical datasets containing RF ablations, resections, and patients with recurrent tumors. The achieved accuracy is 1.2 mm measured as mean internal distance between vessel landmarks and a positive predictive value of 0.95. The quantitative and qualitative results of the outcome validation tool show that in 50% of the cases tumors were only partially covered by the treatment. Conclusion: The use of internal and surface landmarks combined with a physics-based registration method increases the accuracy of the results compared to the accuracy of state of the art methods. An accurate outcome validation tool is important in order to certify that the tumor and its safety margin were fully covered by the treatment. Significance: An accurate outcome validation tool can result in a decrease of the tumor recurrence rate.

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Le Moan, Steven; Tanksale, Tejas Madan; Byshko, Roman; Urban, Philipp

An Observer-Metamerism Sensitivity Index for Electronic Displays

2017

Journal of the Society for Information Display

The effect of observer metamerism induced by electronic displays depends to a large extent on their primary spectra (red, green, and blue in the most common case). In particular, for narrow-band primary spectra whose peak wavelength lies in the range of high variability of the observer's colormatching function, some observers can experience very large differences between actual surface colors (e.g. in a light booth) and displayed colors if the monitor is optimized for the International Commission on Illumination (CIE) 1931 standard observer. However, because narrow-band light-emitting diodes lead to larger color gamuts, more and more monitors with very narrow band primaries are coming onto the market without manufacturers taking into account the associated problem of observer variations. Being able to measure these variations accurately and efficiently is therefore an important objective. In this paper, we propose a new approach to predict the extent of observer metamerism for a particular multiprimary display. Unlike existing dedicated models, ours does not depend on a reference illuminant and a set of reflectance spectra and is computationally more efficient.

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Damer, Naser; Terhörst, Philipp; Braun, Andreas; Kuijper, Arjan

Efficient, Accurate, and Rotation-Invariant Iris Code

2017

IEEE Signal Processing Letters

The large scale of the recently demanded biometric systems has put a pressure on creating a more efficient, accurate, and private biometric solutions. Iris biometrics is one of the most distinctive and widely used biometric characteristics. High-performing iris representations suffer from the curse of rotation inconsistency. This is usually solved by assuming a range of rotational errors and performing a number of comparisons over this range, which results in a high computational effort and limits indexing and template protection. This work presents a generic and parameter-free transformation of binary iris representation into a rotation-invariant space. The goal is to perform accurate and efficient comparison and enable further indexing and template protection deployment. The proposed approach was tested on a database of 10 000 subjects of the ISYN1 iris database generated by CASIA. Besides providing a compact and rotational-invariant representation, the proposed approach reduced the equal error rate by more than 55% and the computational time by a factor of up to 44 compared to the original representation.

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Papaioannou, Georgios; Schreck, Tobias; Andreadis, Anthousis; Mavridis, Pavlos; Gregor, Robert; Sipiran, Ivan; Vardis, Konstantinos

From Reassembly to Object Completion: A Complete Systems Pipeline

2017

ACM Journal on Computing and Cultural Heritage

The problem of the restoration of broken artifacts, where large parts could be missing, is of high importance in archaeology. The typical manual restoration can become a tedious and error-prone process, which also does not scale well. In recent years, many methods have been proposed for assisting the process, most of which target specialized object types or operate under very strict constraints. We propose a digital shape restoration pipeline consisting of proven, robust methods for automatic fragment reassembly and shape completion of generic three-dimensional objects of arbitrary type. In this pipeline, first we introduce a novel unified approach for handling the reassembly of objects from heavily damaged fragments by exploiting both fracture surfaces and salient features on the intact sides of fragments, when available. Second, we propose an object completion procedure based on generalized symmetries and a complementary part extraction process that is suitable for driving the fabrication of missing geometry. We demonstrate the effectiveness of our approach using real-world fractured objects and software implemented as part of the European Union-funded PRESIOUS project, which is also available for download from the project site.

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Weber, Nicolas; Goesele, Michael

MATOG: Array Layout Auto-Tuning for CUDA

2017

ACM Transactions on Architecture and Code Optimization

Optimal code performance is (besides correctness and accuracy) the most important objective in compute intensive applications. In many of these applications, Graphic Processing Units (GPUs) are used because of their high amount of compute power. However, caused by their massively parallel architecture, the code has to be specifically adjusted to the underlying hardware to achieve optimal performance and therefore has to be reoptimized for each new generation. In reality, this is usually not the case as productive code is normally at least several years old and nobody has the time to continuously adjust existing code to new hardware. In recent years more and more approaches have emerged that automatically tune the performance of applications toward the underlying hardware. In this article, we present the MATOG auto-tuner and its concepts. It abstracts the array memory access in CUDA applications and automatically optimizes the code according to the used GPUs. MATOG only requires few profiling runs to analyze even complex applications, while achieving significant speedups over non-optimized code, independent of the used GPU generation and without the need to manually tune the code.

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Hur, Junhwa; Roth, Stefan

MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation

2017

2017 IEEE International Conference on Computer Vision

IEEE International Conference on Computer Vision (ICCV) <16, 2017, Venice, Italy>

Optical flow estimation is one of the most studied problems in computer vision, yet recent benchmark datasets continue to reveal problem areas of today's approaches. Occlusions have remained one of the key challenges. In this paper, we propose a symmetric optical flow method to address the well-known chicken-and-egg relation between optical flow and occlusions. In contrast to many state-of-the-art methods that consider occlusions as outliers, possibly filtered out during post-processing, we highlight the importance of joint occlusion reasoning in the optimization and show how to utilize occlusion as an important cue for estimating optical flow. The key feature of our model is to fully exploit the symmetry properties that characterize optical flow and occlusions in the two consecutive images. Specifically through utilizing forward-backward consistency and occlusion-disocclusion symmetry in the energy, our model jointly estimates optical flow in both forward and backward direction, as well as consistent occlusion maps in both views. We demonstrate significant performance benefits on standard benchmarks, especially from the occlusion-disocclusion symmetry. On the challenging KITTI dataset we report the most accurate two-frame results to date.

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Wannenwetsch, Anne S.; Roth, Stefan; Keuper, Margret

ProbFlow: Joint Optical Flow and Uncertainty Estimation

2017

2017 IEEE International Conference on Computer Vision

IEEE International Conference on Computer Vision (ICCV) <16, 2017, Venice, Italy>

Optical flow estimation remains challenging due to untextured areas, motion boundaries, occlusions, and more. Thus, the estimated flow is not equally reliable across the image. To that end, post-hoc confidence measures have been introduced to assess the per-pixel reliability of the flow. We overcome the artificial separation of optical flow and confidence estimation by introducing a method that jointly predicts optical flow and its underlying uncertainty. Starting from common energy-based formulations, we rely on the corresponding posterior distribution of the flow given the images. We derive a variational inference scheme based on mean field, which incorporates best practices from energy minimization. An uncertainty measure is obtained along the flow at every pixel as the (marginal) entropy of the variational distribution. We demonstrate the flexibility of our probabilistic approach by applying it to two different energies and on two benchmarks. We not only obtain flow results that are competitive with the underlying energy minimization approach, but also a reliable uncertainty measure that significantly outperforms existing post-hoc approaches.

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Oksuz, Ilkay; Mukhopadhyay, Anirban; Dharmakumar, Rohan; Tsaftaris, Sotirios A.

Unsupervised Myocardial Segmentation for Cardiac BOLD

2017

IEEE Transactions on Medical Imaging

A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal patterns in myocardial intensity, but these patterns also trouble supervised segmentation methods, the de facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper, we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace.Our method is based on variational pre-processing and spatial regularization using Markov random fields, to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a data set containing cardiac phase resolved BOLD MR and standard CINE MR image sequences acquired in baseline and is chemic condition across ten canine subjects. Our unsupervised approach outperforms even supervised state-of-the-art segmentation techniques by at least 10% when using Dice to measure accuracy on BOLD data and performs at par for standard CINE MR. Furthermore, a novel segmental analysis method attuned for BOLD time series is utilized to demonstrate the effectiveness of the proposed method in preserving key BOLD patterns.

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Aroudj, Samir; Seemann, Patrick; Langguth, Fabian; Guthe, Stefan; Goesele, Michael

Visibility-Consistent Thin Surface Reconstruction Using Multi-Scale Kernels

2017

ACM Transactions on Graphics

Conference on Computer and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH ASIA) <10, 2017, Bangkok, Thailand>

One of the key properties of many surface reconstruction techniques is that they represent the volume in front of and behind the surface, e.g., using a variant of signed distance functions. This creates significant problems when reconstructing thin areas of an object since the backside interferes with the reconstruction of the front. We present a two-step technique that avoids this interference and thus imposes no constraints on object thickness. Our method first extracts an approximate surface crust and then iteratively refines the crust to yield the final surface mesh. To extract the crust, we use a novel observation-dependent kernel density estimation to robustly estimate the approximate surface location from the samples. Free space is similarly estimated from the samples' visibility information. In the following refinement, we determine the remaining error using a surface-based kernel interpolation that limits the samples' influence to nearby surface regions with similar orientation and iteratively move the surface towards its true location. We demonstrate our results on synthetic as well as real datasets reconstructed using multi-view stereo techniques or consumer depth sensors.