Visual signatures in video visualization

Video visualization is a computation process that extracts meaningful information from original video data sets and conveys the extracted information to users in appropriate visual representations. This paper presents a broad treatment of the subject, following a typical research pipeline involving concept formulation, system development, a path-finding user study, and a field trial with real application data. In particular, we have conducted a fundamental study on the visualization of motion events in videos. We have, for the first time, deployed flow visualization techniques in video visualization. We have compared the effectiveness of different abstract visual representations of videos. We have conducted a user study to examine whether users are able to learn to recognize visual signatures of motions, and to assist in the evaluation of different visualization techniques. We have applied our understanding and the developed techniques to a set of application video clips. Our study has demonstrated that video visualization is both technically feasible and cost-effective. It has provided the first set of evidence confirming that ordinary users can be accustomed to the visual features depicted in video visualizations, and can learn to recognize visual signatures of a variety of motion eventspeer-reviewe

Dynamic Shader Generation for Flexible Multi-Volume Visualization

Imaging) dataset of a human head. The MRI dataset provides the skin and brain tissue. It is vertically cut and the two halves are moved away from each other to get insight to the inner structures. The CTA dataset contains the skull and the vessels which are rendered with different transfer functions. Images (a-c) show three stages of an interactive multi-volume visualization session and image (d) represents the render graph which corresponds to the final configuration in (c). Volume rendering of multiple intersecting volumetric objects is a difficult visualization task, especially if different rendering styles need to be applied to the components, in order to achieve the desired illustration effect. Real-time performance for even complex scenarios is obtained by exploiting the speed and flexibility of modern GPUs, but at the same time programming the necessary shaders turned into a task for GPU experts only. We foresee the demand for an intermediate level of programming abstraction where visualization specialists can realize advanced applications without the need to deal with shader programming intricacies. In this paper, we describe a generic technique for multi-volume render graph. By combining pre-defined nodes, complex volume operations can be realized. Our system efficiently creates GPU-based fragment shader and vertex shader programs “on-thefly” to achieve the desired visual results. We demonstrate the flexibility of our technique by applying several dynamically generated volume rendering styles to multi-modal medical datasets

INTERACTIVE VISUALIZATION OF UNCERTAINTY IN FLOW FIELDS USING TEXTURE-BASED TECHNIQUES ABSTRACT

We describe texture-based flow visualization techniques that bring out the uncertainties in real-world measured flow data or highlight the deviation of scenarios simulated with different numerical techniques. One visualization approach is based on a generic texture-filtering process that improves the perception of uncertainty-affected regions; the other approach focuses on a user-adjusted color coding of uncertainty. Both methods are implemented on graphics hardware and facilitate interactive visualization. The usefulness of these techniques is demonstrated for examples of simulation and PIV data sets.

I.: Visual Signatures in Video Visualization

Abstract — Video visualization is a computation process that extracts meaningful information from original video data sets and conveys the extracted information to users in appropriate visual representations. This paper presents a broad treatment of the subject, following a typical research pipeline involving concept formulation, system development, a path-finding user study and a field trial with real application data. In particular, we have conducted a fundamental study on the visualization of motion events in videos. We have, for the first time, deployed flow visualization techniques in video visualization. We have visual representations of videos. We have conducted a user study to examine whether users are able to learn to recognize visual signatures of motions, and to assist in the evaluation of different visualization techniques. We have applied our understanding and the developed techniques to a set of application video clips. Our study has demonstrated that video visualization is both technically feasible and cost-effective. It provided the first set of evidence confirming that ordinary users can accustom to the visual features depicted in video visualizations, and can learn to recognize visual signatures of a variety of motion events. Index Terms — video visualization, volume visualization, flow visualization, human factors, user study, visual signatures, video processing, optical flow, GPU-rendering. I

GPU-assisted Multi-field Video Volume Visualization

GPU-assisted multi-field rendering provides a means of generating effective video volume visualization that can convey both the objects in a spatiotemporal domain as well as the motion status of these objects. In this paper, we present a technical framework that enables combined volume and flow visualization of a video to be synthesized using GPU-based techniques. A bricking-based volume rendering method is deployed for handling large video datasets in a scalable manner, which is particularly useful for synthesizing a dynamic visualization of a video stream. We have implemented a number of image processing filters, and in particular, we employ an optical flow filter for estimating motion flows in a video. We have devised mechanisms for combining volume objects in a scalar field with glyph and streamline geometry from an optical flow. We demonstrate the effectiveness of our approach with example visualizations constructed from two benchmarking problems in computer vision