Abstract Image Based Flow Visualization for Curved Surfaces

A new method for the synthesis of dense, vector-field aligned textures on curved surfaces is presented, called IBFVS. The method is based on Image Based Flow Visualization (IBFV). In IBFV twodimensional animated textures are produced by defining each frame of a flow animation as a blend between a warped version of the previous image and a number of filtered white noise images. We produce flow aligned texture on arbitrary three-dimensional triangle meshes in the same spirit as the original method: Texture is generated directly in image space. We show that IBFVS is efficient and effective. High performance (typically fifty frames or more per second) is achieved by exploiting graphics hardware. Also, IBFVS can easily be implemented and a variety of effects can be achieved. Applications are flow visualization and surface rendering. Specifically, we show how to visualize the wind field on the earth and how to render a dirty bronze bunny

Visual analytics for multimedia : challenges and opportunities

Understanding huge multimedia collections is a huge challenge. Given a set of hundreds of thousands or millions of images, how to to understand its contents and how to find the images that are relevant for the task at hand? Using a combination of automated methods, visualization, and interaction, known as visual analytics, is probably the only way to go, combining the strengths of man and machine. An overview is given of trends in data visualization and visual analytics is given, and examples of recent work in multimedia analytics are presented. Exploiting meta-data, using interaction with relatively simple visual representations, and alignment with the work flow of users are promising routes, but scalability and evaluation are still challenging

Graph visualization (Invited talk)

Black and white node link diagrams are the classic method to depict graphs, but these often fall short to give insight in large graphs or when attributes of nodes and edges play an important role. Graph visualization aims obtaining insight in such graphs using interactive graphical representations. A variety of ingredients, including color, shape, 3D, shading, and interaction can be used to this end. In this invited talk an overview is given of work on graph visualization of the visualization group of Eindhoven University of Technology, The Netherlands. A wide variety of examples is shown and discussed using demos and animations

Bicubic patches for approximating non-rectangular control-point meshes

The application of surface modelling techniques within solid modelling requires the use of arbitrary meshes of control points to define a solid. A method using bicubic patches is presented which is valid for a number of cases. The faces of the mesh must have four edges, and at all vertices either three or four, or at all vertices an odd number of edges are allowed to meet. Sample applications are presented

Beyond the arrow plot : new methods for flow visualization

Within the realm of CFD-based flow analysis, the presentation of data is a vital issue. Researchers and developers need to gain insight, and non-technical managers and commercial staff have a critical need to understand the true ramifications of the data. Unfortunately, current methods for visualizing flow have limitations that make it difficult to interpret vast quantities of data. New flow visualization techniques, however, can help overcome these limitations and greatly facilitate the interpretation of data for the benefit of technical and non-technical personnel alike. This article explains some of the major limitations of current flow visualization techniques, and introduces the reader to some of the most promising new techniques, including the use of flow probes, texture, stream-surfaces, surface particles, and others which are still undergoing research

ExplainExplore: Visual Exploration of Machine Learning Explanations

Machine learning models often exhibit complex behavior that is difficult to understand. Recent research in explainable AI has produced promising techniques to explain the inner workings of such models using feature contribution vectors. These vectors are helpful in a wide variety of applications. However, there are many parameters involved in this process and determining which settings are best is difficult due to the subjective nature of evaluating interpretability.\u3cbr/\u3eTo this end, we introduce ExplainExplore: an interactive explanation system to explore explanations that fit the subjective preference of data scientists. We leverage the domain knowledge of the data scientist to find optimal parameter settings and instance perturbations, and enable the discussion of the model and its explanation with domain experts.\u3cbr/\u3eWe present a use case on a real-world dataset to demonstrate the effectiveness of our approach for the exploration and tuning of machine learning explanations.\u3cbr/\u3

ExplainExplore: Visual Exploration of Machine Learning Explanations

Machine learning models often exhibit complex behavior that is difficult to understand. Recent research in explainable AI has produced promising techniques to explain the inner workings of such models using feature contribution vectors. These vectors are helpful in a wide variety of applications. However, there are many parameters involved in this process and determining which settings are best is difficult due to the subjective nature of evaluating interpretability.\u3cbr/\u3eTo this end, we introduce ExplainExplore: an interactive explanation system to explore explanations that fit the subjective preference of data scientists. We leverage the domain knowledge of the data scientist to find optimal parameter settings and instance perturbations, and enable the discussion of the model and its explanation with domain experts.\u3cbr/\u3eWe present a use case on a real-world dataset to demonstrate the effectiveness of our approach for the exploration and tuning of machine learning explanations.\u3cbr/\u3eThe Documen