Higher Level Techniques for the Artistic Rendering of Images and Video
Inhaltsverzeichnis
Reference
Collomosse, J.P.: Higher Level Techniques for the Artistic Rendering of Images and Video. PhD thesis, University of Bath, U.K. (2004).
DOI
Abstract
This thesis investigates the problem of producing non-photorealistic renderings for the purpose of aesthetics; so called Artistic Rendering (AR). Specifically, we address the problem of image-space AR, proposing novel algorithms for the artistic rendering of real images and post-production video. Image analysis is a necessary component of image-space AR; information must be ex- tracted from two-dimensional content prior to its re-presentation in some artistic style. Existing image-space AR algorithms perform this analysis at a “low” spatiotemporal level of abstraction. In the case of static AR, the strokes that comprise a rendering are placed independently, and their visual attributes set as a function of only a small image region local to each stroke. In the case of AR animation, video footage is also rendered on a temporally local basis; each frame of animation is rendered taking ac- count of only the current and preceding frame in the video. We argue that this low-level processing paradigm is a limiting factor in the development of image-space AR. The process of deriving artwork from a photograph or video demands visual interpretation, rather than localised filtering, of that source content — a goal challenging enough to warrant application of higher level image analysis techniques, implying interesting new application areas for Computer Vision (and motivating new Computer Vision research as a result). Throughout this thesis we develop a number of novel AR algorithms, the results of which demonstrate a higher spatiotemporal level of analysis to benefit AR in terms of broadening range of potential rendering styles, enhancing temporal coherence in ani- mations, and improving the aesthetic quality of renderings. We introduce the use of global salience measures to image-space AR, and propose novel static AR algorithms which seek to emphasise salient detail, and abstract away unimportant detail within a painting. We also introduce novel animation techniques, describing a “Video Paint- box” capable of creating AR animations directly from video clips. Not only do these animations exhibit a wide gamut of potential styles (such as cartoon-style motion cues and a variety of artistic shading effects), but also exhibit a significant improvement in temporal coherence over the state of the art. We also demonstrate that consideration of the AR process at a higher spatiotemporal level enables the diversification of AR styles to include Cubist-styled compositions and cartoon motion emphasis in animation.
Extended Abstract
Bibtex
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