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

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

Operation of HTS dc-SQUID sensors in high magnetic fields

Most visualization techniques have been designed on the assumption that the data to be represented are free from uncertainty. Yet this is rarely the case. Recently the visualization community has risen to the challenge of incorporating an indication of uncertainty into visual representations, and in this article we review their work. We place the work in the context of a reference model for data visualization, that sees data pass through a pipeline of processes. This allows us to distinguish the visualization of uncertainty - which considers how we depict uncertainty specified with the data - and the uncertainty of visualization - which considers how much inaccuracy occurs as we process data through the pipeline. It has taken some time for uncertain visualization methods to be developed, and we explore why uncertainty visualization is hard - one explanation is that we typically need to find another display dimension and we may have used these up already! To organise the material we return to a typology developed by one of us in the early days of visualization, and make use of this to present a catalogue of visualization techniques describing the research that has been done to extend each method to handle uncertainty. Finally we note the responsibility on us all to incorporate any known uncertainty into a visualization, so that integrity of the discipline is maintained