| Visual signatures in video visualization. | |
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MedLine Citation:
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PMID: 17080839 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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 events. |
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Authors:
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Min Chen; Ralf P Botchen; Rudy R Hashim; Daniel Weiskopf; Thomas Ertl; Ian M Thornton |
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Publication Detail:
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Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: IEEE transactions on visualization and computer graphics Volume: 12 ISSN: 1077-2626 ISO Abbreviation: IEEE Trans Vis Comput Graph Publication Date: 2006 Sep-Oct |
Date Detail:
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Created Date: 2006-11-03 Completed Date: 2007-01-12 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9891704 Medline TA: IEEE Trans Vis Comput Graph Country: United States |
Other Details:
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Languages: eng Pagination: 1093-100 Citation Subset: IM |
Affiliation:
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Computer Science, Swansea University. m.chen@swansea.ac.uk |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Computer Graphics* Female Humans Image Enhancement / methods* Image Interpretation, Computer-Assisted / methods Imaging, Three-Dimensional / methods Information Storage and Retrieval / methods Male Movement* Pattern Recognition, Visual / physiology* Task Performance and Analysis User-Computer Interface* Video Recording / methods* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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