arXiv subjects

Lars König

Publications and source records attributed to Lars König.

3 recordsLinked to original sources

arXiv2026

Extending Triple Graph Grammars to Formalize Complex View Definitions on Families of Models - Long Version

View-based development is an important technique to manage complexity and facilitate cooperation in the development of modern and complex systems of systems. This approach places high demands on view definition languages used in the development process. First, such a language needs to be approachable and extremely versatile so that developers from diverse backgrounds are able to use it and can define views for a diverse set of different tasks. Yet, the language also needs to have a precise and formal semantics so that it is possible to integrate the work performed on various views in a controlled manner to obtain a coherent overall system. In this paper, we continue previous work of equipping the NeoJoin view definition language with a formal semantics that is based on triple graph grammars (TGGs). This paves the way for obtaining automated and incremental synchronization procedures between models and views that come with high formal guarantees for their behavior. Simultaneously, our work serves as a further case study of the expressivity and usability of TGGs. We identify one gap, namely convenient support for the translation of overlapping queries from the view definition language, and tackle that gap by introducing a skip semantics for TGG rules, i.e., allowing to skip certain actions a rule prescribes, depending on the application context.

cs.SE↗

arXiv2018

Fully-deformable 3D image registration in two seconds

We present a highly parallel method for accurate and efficient variational deformable 3D image registration on a consumer-grade graphics processing unit (GPU). We build on recent matrix-free variational approaches and specialize the concepts to the massively-parallel manycore architecture provided by the GPU. Compared to a parallel and optimized CPU implementation, this allows us to achieve an average speedup of 32.53 on 986 real-world CT thorax-abdomen follow-up scans. At a resolution of approximately $256^3$ voxels, the average runtime is 1.99 seconds for the full registration. On the publicly available DIR-lab benchmark, our method ranks third with respect to average landmark error at an average runtime of 0.32 seconds.

cs.CV↗

arXiv2018

A matrix-free approach to parallel and memory-efficient deformable image registration

We present a novel computational approach to fast and memory-efficient deformable image registration. In the variational registration model, the computation of the objective function derivatives is the computationally most expensive operation, both in terms of runtime and memory requirements. In order to target this bottleneck, we analyze the matrix structure of gradient and Hessian computations for the case of the normalized gradient fields distance measure and curvature regularization. Based on this analysis, we derive equivalent matrix-free closed-form expressions for derivative computations, eliminating the need for storing intermediate results and the costs of sparse matrix arithmetic. This has further benefits: (1) matrix computations can be fully parallelized, (2) memory complexity for derivative computation is reduced from linear to constant, and (3) overall computation times are substantially reduced. In comparison with an optimized matrix-based reference implementation, the CPU implementation achieves speedup factors between 3.1 and 9.7, and we are able to handle substantially higher resolutions. Using a GPU implementation, we achieve an additional speedup factor of up to 9.2. Furthermore, we evaluated the approach on real-world medical datasets. On ten publicly available lung CT images from the DIR-Lab 4DCT dataset, we achieve the best mean landmark error of 0.93 mm compared to other submissions on the DIR-Lab website, with an average runtime of only 9.23 s. Complete non-rigid registration of full-size 3D thorax-abdomen CT volumes from oncological follow-up is achieved in 12.6 s. The experimental results show that the proposed matrix-free algorithm enables the use of variational registration models also in applications which were previously impractical due to memory or runtime restrictions.

cs.CV↗