The Library
Modality based perception for selective rendering
Tools
Harvey, Carlo (2011) Modality based perception for selective rendering. PhD thesis, University of Warwick.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://webcat.warwick.ac.uk/record=b2585525~S1
Abstract
A major challenge in generating high-fidelity virtual environments for use in Virtual Reality (VR) is to be able to provide interactive rates of realism. The high-fidelity simulation of light and sound wave propagation is still unachievable in real-time. Physically accurate simulation is very computationally demanding.
Only recently has visual perception been used in high-fidelity rendering to improve performance by a series of novel exploitations; to render parts of the scene that are not currently being attended by the viewer at a much lower quality with-out the difference being perceived. This thesis investigates the effect spatialized directional sounds, both discrete and converged and smells have on the visual attention of the user towards rendered scene images. These perceptual artefacts are utilised in selective rendering pipelines via the use of multi-modal maps.
This work verifies the worth of investigating subliminal saccade shifts (fast movements of the eyes) from directional audio impulses via a pilot study to eye track participant's free viewing a scene with and without an audio impulse and with and without a congruency for that impulse. This experiment showed that even without an acoustic identifier in the scene, directional sound provides an impulse to guide subliminal saccade shifts. A novel technique for generating interactive discrete acoustic samples from arbitrary geometry is also presented.
This work is extrapolated by investigating whether temporal auditory sound wave saliencies can be used as a feature vector in the image rendering process. The method works by producing image maps of the sound wave flux and attenuating this map via these auditory saliency feature vectors. Whilst selectively rendering, the method encodes spatial auditory distracters into the standard visual saliency map.
Furthermore, this work investigates the effect various smells have on the visual attention of a user when free viewing a set of images whilst being eye tracked.
This thesis explores these saccade shifts to a congruent smell object. By analysing the gaze points, the time spent attending a particular area of a scene is considered. The work presents a technique derived from measured data to modulate traditional saliency maps of image features to account for the observed results for smell congruences and shows that smell provides an impulse on visual attention.
Finally, the observed data is used in applying modulated image saliency maps to address the additional effects cross-modal stimuli has on human perception when applied to a selective renderer. These multi-modal maps, derived from measured data for smells, and from sound spatialisation techniques attempt to exploit the extra stimuli presented in multi-modal VR environments and help to re-quantify the saliency map to account for observed cross-modal perceptual features of the human visual system. The multi-modal maps are tested through rigorous psychophysical experiments to examine their applicability to selective rendering algorithms, with a series of fixed cost rendering functions, and are found to perform better than image saliency maps that are naively applied to multi-modal virtual environments.
Item Type: | Thesis (PhD) | ||||
---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
||||
Library of Congress Subject Headings (LCSH): | Virtual reality | ||||
Official Date: | November 2011 | ||||
Dates: |
|
||||
Institution: | University of Warwick | ||||
Theses Department: | School of Engineering | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Chalmers, Alan | ||||
Extent: | xiv, 220 leaves : illustrations, charts | ||||
Language: | eng |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |