Volume Visualization Using Principal Component Analysis
| dc.contributor.author | Alakkari, Salaheddin | en_US |
| dc.contributor.author | Dingliana, John | en_US |
| dc.contributor.editor | Stefan Bruckner and Bernhard Preim and Anna Vilanova and Helwig Hauser and Anja Hennemuth and Arvid Lundervold | en_US |
| dc.date.accessioned | 2016-09-07T05:37:28Z | |
| dc.date.available | 2016-09-07T05:37:28Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | In this paper, we investigate the use of Principal Component Analysis (PCA) for image-based volume visualization. Firstly we compute a high-dimensional eigenspace using training images, pre-rendered using a standard ray-caster, from a spherically distributed range of camera positions. Then, our system is able to synthesize arbitrary views of the dataset with minimal computation at runtime. We propose a perceptually-adaptive technique to minimize data size and computational complexity whilst preserving perceptual quality of the visualization, in comparison to corresponding ray-cast images. Results indicate that PCA is able to sufficiently learn the full view-independent volumetric model through a finite number of training images and generalize the computed eigenspace to produce high quality images from arbitrary viewpoints, on demand. The approach has potential application in client-server volume visualization or where results of a computationally-complex 3D imaging process need to be interactively visualized on a display device of limited specification. | en_US |
| dc.description.sectionheaders | Novel Visualization Techniques (Short Papers) | |
| dc.description.seriesinformation | Eurographics Workshop on Visual Computing for Biology and Medicine | |
| dc.identifier.doi | 10.2312/vcbm.20161271 | |
| dc.identifier.isbn | 978-3-03868-010-9 | |
| dc.identifier.issn | 2070-5786 | |
| dc.identifier.pages | 53-57 | |
| dc.identifier.uri | https://doi.org/10.2312/vcbm.20161271 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/vcbm20161271 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Volume Visualization Using Principal Component Analysis | en_US |
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