Torsten Ullrich

Hierarchical spherical distance fields for collision detection

The problem of collision detection between objects is fundamental in many different communities including CAD, robotics, computer graphics, and computational geometry. This article presents a fast collision detection technique for all types of rigid bodies, demonstrated using polygon soups. We present two algorithms for computing a discrete spherical distance field of models. For compactly storing the distance field, we use a subsampling filter bank.

Visual analytics for concept exploration in subspaces of patient groups

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

Analysis of Patient Groups and Immunization Results Based on Subspace Clustering

Biomedical experts are increasingly confronted with what is often called Big Data, an important subclass of high-dimensional data. High-dimensional data analysis can be helpful in finding relationships between records and dimensions. However, due to data complexity, experts are decreasingly capable of dealing with increasingly complex data. Mapping higher dimensional data to a smaller number of relevant dimensions is a big challenge due to the curse of dimensionality. Irrelevant, redundant, and conflicting dimensions affect the effectiveness and efficiency of analysis. Furthermore, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We show the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we analyze relationships between patients, sets of patient attributes, and outcomes of a vaccination treatment by means of a subspace clustering approach. We present an analysis workflow and discuss future directions for high-dimensional (medical) data analysis and visual exploration.

Modeling procedural knowledge: a generative modeler for cultural heritage

Within the last few years generative modeling techniques have gained attention especially in the context of cultural heritage. As a generative model describes a rather ideal object than a real one, generative techniques are a basis for object description and classification. This procedural knowledge differs from other kinds of knowledge, such as declarative knowledge, in a significant way. It can be applied to a task. This similarity to algorithms is reflected in the way generative models are designed: they are programmed. In order to make generative modeling accessible to cultural heritage experts, we created a generative modeling framework which accounts for their special needs. The result is a generative modeler (http://www.cgv.tugraz.at/euclides) based on an easy-to-use scripting language (JavaScript). The generative model meets the demands on documentation standards and fulfils sustainability conditions. Its integrated meta-modeler approach makes it independent from hardware, software and platforms.

Information technology for cultural heritage

Information technology applications in the field of cultural heritage include various disciplines of computer science. The work flow from archaeological discovery to scientific preparation demands multidisciplinary cooperation and interaction at various levels. This article describes the information technology pipeline from the computer science point of view. The description starts with the model acquisition. Computer vision algorithms are able to generate a raw three-dimensional (3D) model using input data such as photos and scans. In the next step, computer graphics methods create an accurate, level model description.

Discovering medical knowledge using visual analytics: a survey on methods for systems biology and *-omics data

Due to advanced technologies, the amount of biomedical data has been increasing drastically. Such large data sets might be obtained from hospitals, medical practices or laboratories and can be used to discover unknown knowledge and to find and reflect hypotheses. Based on this fact, knowledge discovery systems can support experts to make further decisions, explore the data or to predict future events. To analyze and communicate such a vast amount of information to the user, advanced techniques such as knowledge discovery and information visualization are necessary. Visual analytics combines these fields and supports users to integrate domain knowledge into the knowledge discovery process. This article gives a state-of-the-art overview on visual analytics reseach with a focus on the biomedical domain, systems biology and *omics data.

A Survey of Algorithmic Shapes

In the context of computer-aided design, computer graphics and geometry processing, the idea of generative modeling is to allow the generation of highly complex objects based on a set of formal construction rules. Using these construction rules, a shape is described by a sequence of processing steps, rather than just by the result of all applied operations: shape design becomes rule design. Due to its very general nature, this approach can be applied to any domain and to any shape representation that provides a set of generating functions. The aim of this survey is to give an overview of the concepts and techniques of procedural and generative modeling, as well as their applications with a special focus on archeology and architecture.

Two different views on collision detection

In this article, we present two algorithms for precise collision detection between two potentially colliding objects. The first one uses axis-aligned bounding boxes (AABB) and is a typical representative of a computational geometry algorithm. The second one uses spherical distance fields originating in image processing. Both approaches addresses the following challenges of collision detection algorithms: just in time, little resources, inclusive etc. Thus both approaches are scalable in the information they give in collision determination and the analysis up to a fixed refinement level, the collision time depends on the granularity of the bounding volumes and it is also possible to estimate the time bounds for the collision test tightly

Simple and Efficient Normal Encoding with Error Bounds

Normal maps and bump maps are commonly used techniques to make 3D scenes more realistic. Consequently, the efficient storage of normal vectors is an important task in computer graphics. This work presents a fast, lossy compression/decompression algorithm for arbitrary resolutions. The complete source code is listed in the appendix and is ready to use.

Compilation of procedural models

Scripting techniques are used in various contexts. The field of application ranges from layout description languages (PostScript), user interface description languages (XUL) and classical scripting languages (JavaScript) to action nodes in scene graphs (VRMLScript) and web-based desktop applications (AJAX). All these applications have an increase of scripted components in common -- especially in computer graphics. As the interpretation of a geometric script is computationally more intensive than the handling of static geometry, optimization techniques, such as just-in-time compilation, are of great interest. Unfortunately, scripting languages tend to support features such as higher order functions or self-modification, etc. These language characteristic are difficult to compile into machine/byte-code. Therefore, we present a hybrid approach: an interpreter with an integrated compiler. In this way we speed up the script evaluation without having to remove any language features e.g. the possibility of self-modifications. We demonstrate its usage at XGML -- a dialect of the generative modeling language GML, which is characterized by its dynamic behavior.