Title of Thesis
"Evaluating Perceptual Effects of Non-photorealistic
>> Now that we have pretty images - what do we do with them? <<
>> The following is my original abridged research proposal. A pdf version can be found here. If you require the longer version, please contact me (see cv page for details). As anyone who has been involved in a long-term research project knows, ideas, concepts and objectives tend to change and develop over time. If you would like to know more about how things have evolved in the meantime please feel free to contact me.<<
Historically, the field of Computer Graphics has evolved around the idea of producing images that are as realistic as possible , coining the term photorealism. Great advances have been made in this respect and, as technology improves or becomes available, will be made for some time to come. We believe that in most cases (apart from very few simulation applications) such realistic images are not an end in themselves. They were created for a specific audience, a particular viewer. They were created for architects, designers, medical staff, movie-goers, etc. In other words, they were created to be looked at, scrutinised or enjoyed by humans. For a very long time this fact has been largely ignored, one argument being that if an image is as real as possible, it resembles reality and thus requires no tailoring for a given observer (we consider this a fallacy, as the mere act of choosing a particular scene, subject, view-point, lighting, etc. already customises the content and visual composition of an image to a large degree).
One recent development which is trying to complement the merits of existing
realistic graphics, and which is quickly gaining momentum in the scientific
community is that of Non-photorealistic Rendering (NPR). NPR has been attributed
with many desirable qualities, chiefly its abilities of representing abstract
data and ideas (, , ),
of realizing expressive styles (, ,
, , ), of
evoking emotional responses (, ,
), and of pertaining to low-level perceptual processes
(, , ).
It is often argued, that the abovementioned qualities might be somewhat interlinked (e.g. the clarity/economy of line in sketches which is believed to appeal to low-level visual processes helps in abstracting from a concrete form), but not much is known about the correlations between these qualities. Recently, we see computer-generated NPR being employed in ever increasing form, including the latest PIXAR releases, computer-games such as XIII and Teenage Mutant Ninja Turtles, 3D modelling aids such as Teddy and Jot (, ) to name but a few. This trend is likely to continue for some time to come, as technical and theoretical advances in NPR and related fields are made.
One of the most exciting developments with respect to NPR is the advent of user-programmable graphics processing units (GPUs). This new technology allows for (limited, but expanding) access to the graphics pipeline, thus permitting the creation of endless variations of shaders and renderers. Many special NPR effects which used to be difficult to implement (e.g. , , , , , ) can now be implemented in a few lines of portable, specialized graphics languages (CG, HLSL), even though speed and versatility are still issues on lower end graphics cards. We anticipate these problems to be overcome in the near future. One upshot of this development will be an increased ease of creating and implementing NPR renderers and effects. As a result, NPR will increasingly find its way onto desktop computers in numerous guises.
We therefore return to our initial statement; that computer generated images are generally created for humans. This is especially true for NPR images (we find it difficult to conceive of a situation where, say, a water-color rendition of network-traffic would be analyzed by a secondary program). Currently, only very little research has been undertaken into the perceptual effects of NPR, i.e. how NPR images are actually perceived and how this affects interaction with NPR environments (, , ). It is our aim to address this issue.
Using our background in NPR research  and combining this with work in human perception (e.g. , , , , ) we are in the process of building specialised renderers, which address perceptual modules of the human visual system. User-studies are then to be employed to test various virtual environment (VE) settings and evaluate them with respect to interactivity measures as compared to more traditionally rendered VEs. The analysis of our tests will help identify areas of perception and interaction differences on various levels. This in turn will assist in constructing guidelines for use with NPR environments.
After significant advances in NPR theory and technology have been brought forward in all the recent major graphics-related conferences, we believe it is time to research meta-aspects of NPR, such as perception and interaction with NPR environments. We hope that by doing this, we are able to contribute to the understanding of why and where NPR can be gainfully employed and what problems are associated with its use.
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