Interactive multilevel focus+context visualization framework
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- 作者：Mahmudul Hasan ; Faramarz F. Samavati ; Christian Jacob
- 关键词：Focus+context visualization ; Contextual close ; up ; Multilevel hierarchy ; Balanced decomposition ; Perfect reconstruction ; Balanced wavelet transform
- 刊名：The Visual Computer
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：32
- 期：3
- 页码：323-334
- 全文大小：2,196 KB
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Faramarz F. Samavati (1)
Christian Jacob (1) (2)
1. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
2. Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada - 刊物类别：Computer Science
- 刊物主题：Computer Graphics
Computer Science, general
Artificial Intelligence and Robotics
Image Processing and Computer Vision - 出版者：Springer Berlin / Heidelberg
- ISSN：1432-2315
文摘
In this article, we present the construction of an interactive multilevel focus+context visualization framework for the navigation and exploration of large-scale 2D and 3D images. The presented framework utilizes a balanced multiresolution technique supported by a balanced wavelet transform (BWT). It extends the mode of focus+context visualization, where spatially separate magnification of regions of interest (ROIs) is performed, as opposed to in-place magnification. Each resulting visualization scenario resembles a tree structure, where the root constitutes the main context, each non-root internal node plays the dual roles of both focus and context, and each leaf solely represents a focus. Our developed prototype supports interactive manipulation of the visualization hierarchy, such as addition and deletion of ROIs and desired changes in their resolutions at any level of the hierarchy on the fly. We describe the underlying data structure efficiently support such operations. Changes in the spatial locations of query windows defining the ROIs trigger on-demand reconstruction queries. We explain in detail how to efficiently process such reconstruction queries within the hierarchy of details (wavelet coefficients) contained in the BWT in order to ensure real-time feedback. As the BWT need only be constructed once in a preprocessing phase on the server-side and robust on-demand reconstruction queries require minimal data communication overhead, our presented framework is a suitable candidate for efficient web-based visualization of complex large-scale imagery. We also discuss the performance characteristics of our proposed framework from various aspects, such as time and space complexities and achieved frame rates.