Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638701
Title: Refraction in volume graphics
Author: Rodgman, D. N.
Awarding Body: University of Wales Swansea
Current Institution: Swansea University
Date of Award: 2004
Availability of Full Text:
Access through EThOS:
Abstract:
In this study, we have proposed several methods for sampling field-based representations of refraction attributes, and analysed these methods in terms of the quality and accuracy of the results produced. We have identified two types of anomalies that affect the correctness of some methods, and found that the best of these methods is capable of producing images of equal quality to surface-graphics techniques. Our approach produces good results with functionally defined models, and discrete volumetric data, in the absence of noise. We have also established that this approach allows the modelling and rendering of types of objects that cannot be represented in surface graphics (e.g. objects with a continuous, non-uniform refractive index). Where noise is present in volumetric data, it significantly affects the correctness of rendering refraction, usually resulting in images of poor quality. A number of methods for smoothing have been examined, including low-pass filtering and various types of nonlinear diffusion. We have shown that regularised anisotropic nonlinear diffusion is a powerful and effective method for processing noisy volumetric data sets in order to improve the quality of images featuring refraction. High quality images featuring refraction often take a long time to render. We have presented the design and implementation of a parallel, open source volume graphics rendered which supports refraction and multi-volume scenes. A large number of issues related to a efficient operation of a parallel renderer have been examined, and a high-performance, scaleable parallel renderer has been developed, which performs efficiently on both networked clusters, and shared memory multi-processor machines.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.638701  DOI: Not available
Share: