AlSub: Fully Parallel and Modular Subdivision

In recent years, mesh subdivision---the process of forging smooth free-form surfaces from coarse polygonal meshes---has become an indispensable production instrument. Although subdivision performance is crucial during simulation, animation and rendering, state-of-the-art approaches still rely on serial implementations for complex parts of the subdivision process. Therefore, they often fail to harness the power of modern parallel devices, like the graphics processing unit (GPU), for large parts of the algorithm and must resort to time-consuming serial preprocessing. In this paper, we show that a complete parallelization of the subdivision process for modern architectures is possible. Building on sparse matrix linear algebra, we show how to structure the complete subdivision process into a sequence of algebra operations. By restructuring and grouping these operations, we adapt the process for different use cases, such as regular subdivision of dynamic meshes, uniform subdivision for immutable topology, and feature-adaptive subdivision for efficient rendering of animated models. As the same machinery is used for all use cases, identical subdivision results are achieved in all parts of the production pipeline. As a second contribution, we show how these linear algebra formulations can effectively be translated into efficient GPU kernels. Applying our strategies to $\sqrt{3}$, Loop and Catmull-Clark subdivision shows significant speedups of our approach compared to state-of-the-art solutions, while we completely avoid serial preprocessing.Comment: Changed structure Added content Improved description

Siamese neural network for motion detection in video sequences

We examine the problem of automatic motion detection in video sequences captured by video surveillance systems. The state of the art methods use convolutional neural networks. Their main limitation is that they need to be retrained if they are to be applied on different sequences. In our thesis, we present a novel method which is based on the architecture of siamese convolutional neural networks. Our network semantically describes the input image from the sequence and the model of the background of the sequence. It does this by using the siamese architecture. It then applies convolutional layers to detect relevant differences and generates the final probability segmentation mask. Our approach allows detection on different video sequences without retraining the network on each new sequence. To detect motion only a reference background images is required. The method automatically updates the background image during application. We trained our network on the CDNET data set. We compared our method with the other methods published on the CDNET website. It ranked as the eight best method of the 46 published methods. We also evaluated our method on the Wallflower and SGM-RGBD data sets. There, we tested it in different circumstances and provided qualitative analysis of its performance

Siamese neural network for motion detection in video sequences

We examine the problem of automatic motion detection in video sequences captured by video surveillance systems. The state of the art methods use convolutional neural networks. Their main limitation is that they need to be retrained if they are to be applied on different sequences. In our thesis, we present a novel method which is based on the architecture of siamese convolutional neural networks. Our network semantically describes the input image from the sequence and the model of the background of the sequence. It does this by using the siamese architecture. It then applies convolutional layers to detect relevant differences and generates the final probability segmentation mask. Our approach allows detection on different video sequences without retraining the network on each new sequence. To detect motion only a reference background images is required. The method automatically updates the background image during application. We trained our network on the CDNET data set. We compared our method with the other methods published on the CDNET website. It ranked as the eight best method of the 46 published methods. We also evaluated our method on the Wallflower and SGM-RGBD data sets. There, we tested it in different circumstances and provided qualitative analysis of its performance

Ensemble weather forecast post-processing with a flexible probabilistic neural network approach

Ensemble forecast post-processing is a necessary step in producing accurate probabilistic forecasts. Conventional post-processing methods operate by estimating the parameters of a parametric distribution, frequently on a per-location or per-lead-time basis. We propose a novel, neural network-based method, which produces forecasts for all locations and lead times, jointly. To relax the distributional assumption of many post-processing methods, our approach incorporates normalizing flows as flexible parametric distribution estimators. This enables us to model varying forecast distributions in a mathematically exact way. We demonstrate the effectiveness of our method in the context of the EUPPBench benchmark, where we conduct temperature forecast post-processing for stations in a sub-region of western Europe. We show that our novel method exhibits state-of-the-art performance on the benchmark, outclassing our previous, well-performing entry. Additionally, by providing a detailed comparison of three variants of our novel post-processing method, we elucidate the reasons why our method outperforms per-lead-time-based approaches and approaches with distributional assumptions

Layered Fields for Natural Tessellations on Surfaces

Mimicking natural tessellation patterns is a fascinating multi-disciplinary problem. Geometric methods aiming at reproducing such partitions on surface meshes are commonly based on the Voronoi model and its variants, and are often faced with challenging issues such as metric estimation, geometric, topological complications, and most critically parallelization. In this paper, we introduce an alternate model which may be of value for resolving these issues. We drop the assumption that regions need to be separated by lines. Instead, we regard region boundaries as narrow bands and we model the partition as a set of smooth functions layered over the surface. Given an initial set of seeds or regions, the partition emerges as the solution of a time dependent set of partial differential equations describing concurrently evolving fronts on the surface. Our solution does not require geodesic estimation, elaborate numerical solvers, or complicated bookkeeping data structures. The cost per time-iteration is dominated by the multiplication and addition of two sparse matrices. Extension of our approach in a Lloyd's algorithm fashion can be easily achieved and the extraction of the dual mesh can be conveniently preformed in parallel through matrix algebra. As our approach relies mainly on basic linear algebra kernels, it lends itself to efficient implementation on modern graphics hardware.Comment: Natural tessellations, surface fields, Voronoi diagrams, Lloyd's algorith

SMIXS: Novel efficient algorithm for non-parametric mixture regression-based clustering

We investigate a novel non-parametric regression-based clustering algorithm for longitudinal data analysis. Combining natural cubic splines with Gaussian mixture models (GMM), the algorithm can produce smooth cluster means that describe the underlying data well. However, there are some shortcomings in the algorithm: high computational complexity in the parameter estimation procedure and a numerically unstable variance estimator. Therefore, to further increase the usability of the method, we incorporated approaches to reduce its computational complexity, we developed a new, more stable variance estimator, and we developed a new smoothing parameter estimation procedure. We show that the developed algorithm, SMIXS, performs better than GMM on a synthetic dataset in terms of clustering and regression performance. We demonstrate the impact of the computational speed-ups, which we formally prove in the new framework. Finally, we perform a case study by using SMIXS to cluster vertical atmospheric measurements to determine different weather regimes