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The Power of the Image

Emotion, Expression, Explanation


András Benedek and Kristof Nyiri

We think primarily in images, and only secondarily in words, while both the image and the word are preceded by the bodily, the visceral, the muscular. This holds even for mathematical thinking. It is the entire motor system, including facial expressions and bodily gestures, that underlies not just emotions but also abstract thought. Communication, too, is a primordially visual task, spoken and written language only gradually supplementing and even supplanting the pictorial. Writing liberates, but also enslaves; after centuries of a dominantly verbal culture, today the ease of producing and accessing digital images amounts to a homecoming of the visual, with the almost limitless online availability of our textual heritage completing the educational revolution of the 21st century.
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Computational Aesthetics for Rendering Virtual Scenes on 3D Stereoscopic Displays


László Szirmay-Kalos – Pirkko Oittinen – Balázs Teréki

Computer graphics builds virtual scenes that are rendered presenting images to the user. Both the virtual world and the image are represented by numbers in the computer, and there are many different possibilities to define a mapping between these numbers. What we usually need is a meaningful image that can be interpreted by the user without reading lengthy user manuals. To reach this goal, we use analogies like photography in photorealistic rendering,1 drawing or painting in illustrative rendering,2 X-ray, flows, etc. in abstract data visualization, because such images are natural and need no additional explanation (Figure 1). The analog process uses not only the primary data of the virtual scene but also additional parameters, called studio objects, defining how rendering should take place. Studio objects include camera and light parameters in photorealistic image synthesis, and drawing or hatching styles in illustrative rendering. Different studio object settings lead to different images that are better or poorer in characterizing the scene. To find good studio object settings that lead to expressive images, users usually initiate a long manual search based on trial and error.

Figure 1: Images obtained with photorealistic (left) and illustrative (right) rendering methods

← 187 | 188 → This long procedure can be replaced or helped by automatic algorithms that search for good rendering parameters. To support the search process, the quality of the images should be characterized by numeric values, and we...

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