Julius Hannink

Locally Adaptive Frames in the Roto-Translation Group and Their Applications in Medical Imaging

Locally adaptive differential frames (gauge frames) are a well-known effective tool in image analysis, used in differential invariants and PDE-flows. However, at complex structures such as crossings or junctions, these frames are not well defined. Therefore, we generalize the notion of gauge frames on images to gauge frames on data representations

Multi-view representation learning via gcca for multimodal analysis of Parkinson's disease

U:\mathbb {R}^{d} \rtimes S^{d-1} \rightarrow \mathbb {R}

Activity recognition in beach volleyball using a Deep Convolutional Neural Network

U:Rd⋊Sd-1→R defined on the extended space of positions and orientations, which we relate to data on the roto-translation group SE(d),

Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis

d=2,3

On the impact of non-modal phonation on phonological features

d=2,3. This allows to define multiple frames per position, one per orientation. We compute these frames via exponential curve fits in the extended data representations in SE(d). These curve fits minimize first- or second-order variational problems which are solved by spectral decomposition of, respectively, a structure tensor or Hessian of data on SE(d). We include these gauge frames in differential invariants and crossing-preserving PDE-flows acting on extended data representation U and we show their advantage compared to the standard left-invariant frame on SE(d). Applications include crossing-preserving filtering and improved segmentations of the vascular tree in retinal images, and new 3D extensions of coherence-enhancing diffusion via invertible orientation scores.

Acute Neuromuscular Adaptations in the Postural Control of Patients with Parkinson’s Disease after Perturbed Walking

Objective: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of stateof-the-art double integration approaches to gait patterns with a clear zero-velocity phase. Methods: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a 10-fold cross validation and for three different stride definitions. Results: Even though best results are achieved with strides defined from mid-stance to mid-stance with average accuracy and precision of 0.01±5.37 cm, performance does not strongly depend on stride definition. The achieved precision outperforms stateof-the-art methods evaluated on the same benchmark dataset by 3.0 cm (36%). Conclusion: Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. Significance: With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by re-training and applying the proposed method.