Matthew van der Zwan

Illustrative molecular visualization with continuous abstraction

Molecular systems may be visualized with various degrees of structural abstraction, support of spatial perception, and ‘illustrativeness.’ In this work we propose and realize methods to create seamless transformations that allow us to affect and change each of these three parameters individually. The resulting transitions give viewers a dedicated control of abstraction in illustrative molecular visualization and, consequently, allow them to seamlessly explore the resulting abstraction space for obtaining a fundamental understanding of molecular systems. We show example visualizations created with our approach and report informal feedback on our technique from domain experts.

CUBu: Universal Real-Time Bundling for Large Graphs

Visualizing very large graphs by edge bundling is a promising method, yet subject to several challenges: speed, clutter, level-of-detail, and parameter control. We present CUBu, a framework that addresses the above problems in an integrated way. Fully GPU-based, CUBu bundles graphs of up to a million edges at interactive framerates, being over 50 times faster than comparable state-of-the-art methods, and has a simple and intuitive control of bundling parameters. CUBu extends and unifies existing bundling techniques, offering ways to control bundle shapes, separate bundles by edge direction, and shade bundles to create a level-of-detail visualization that shows both the graph core structure and its details. We demonstrate CUBu on several large graphs extracted from real-life application domains.

Continuous Navigation of Nested Abstraction Levels

We investigate the dedicated control of multiple levels of semantic and sampling-based abstraction in 3D datasets, i.e., different types of data abstractions as opposed to sampling-based abstraction which shows more or less data. This dedicated navigation in the abstraction space facilitates the mental integration of different existing visualization techniques in many application areas including our example domain of fluid simulation. We realize the continuous abstraction control by interpolating between the levels while being able to simultaneously show multiple abstractions. We employ a halo-like shading technique based on distance fields to blend between several levels while continuously navigating between focus and context abstractions. We further add a semantic lens to find focus abstractions close to a user-defined context abstraction. Our entire implementation uses 2D image-based techniques to enable real-time performance, which seamlessly integrates within a 3D visualization tool.

Robust and Fast Teat Detection and Tracking in Low-resolution Videos for Automatic Milking Devices

We present a system for detection and tracking of cow teats, as part of the construction of automatic milking devices (AMDs) in the dairy industry. We detail algorithmic solutions for the robust detection and tracking of teat tips in low-resolution video streams produced by embedded time-of-flight cameras, using a combination of depth images and point-cloud data. We present a visual analysis tool for the validation and optimization of the proposed techniques. Compared to existing state-of-the-art solutions, our method can robustly handle occlusions, variable poses, and geometries of the tracked shape, and yields a correct tracking rate for over 90% for tests involving real-world images obtained from an industrial AMD robot.