Year: 2011
Type: article
Abstract: Recent advances in sophisticated computational techniques have facilitated simulation of incrediblydetailed time-varying trajectories and in the process have generated vast quantities of simulation data. The current tools to analyze and comprehend large-scale time-varying data, however, lag far behind our ability to produce such simulation data. Saliency-based analysis can be applied to time-varying 3D datasets for the purpose of summarization, abstraction, and motion analysis. As the sizes of time-varying datasets continue to grow, it becomes more and more difficult to comprehend vast amounts of data and information in a short period of time. In this paper, we use eigenanalysis to generate orthogonal basis functions over sliding windows to characterize regions of unusual deviations and significant trends. Our results show that motion subspaces provide an effective technique for summarization of large molecular dynamics trajectories.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: High Performance and Visualization Cluster for Research in Coupled Computational Steering and Visualization for Large Scale Applications, G2976337622, Functional Cycle of a Mechanosensitive Channel, Saliency-guided Graphics and Visualization, Autostereoscopic Visualization and Geometric Computing for Biological Macromolecules
OpenAlex ID: W2168040702