32-Issue 8

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https://diglib.eg.org/handle/10.2312/186

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Browsing 32-Issue 8 by Subject "and systems"

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    An Algorithm for Random Fractal Filling of Space
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Shier, John; Bourke, Paul; Holly Rushmeier and Oliver Deussen
    Computational experiments with a simple algorithm show that it is possible to fill any spatial region with a random fractalization of any shape, with a continuous range of pre‐specified fractal dimensions D. The algorithm is presented here in 1, 2 or 3 physical dimensions. The size power‐law exponent c or the fractal dimension D can be specified ab initio over a substantial range. The method creates an infinite set of shapes whose areas (lengths, volumes) obey a power law and sum to the area (length and volume) to be filled. The algorithm begins by randomly placing the largest shape and continues using random search to place each smaller shape where it does not overlap or touch any previously placed shape. The resulting gasket is a single connected object.Computational experiments with a simple algorithm show that it is possible to fill any spatial region with a random fractalization Q1 of any shape, with a continuous range of pre‐specified fractal dimensions D. The algorithm is presented here in 1, 2 or 3 physical dimensions. The size power‐law exponent c or the fractal dimension D can be specified ab initio over a substantial range. The method creates an infinite set of shapes whose areas (lengths, volumes) obey a power law and sum to the area (length and volume) to be filled.
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    Non-Oriented MLS Gradient Fields
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Chen, Jiazhou; Guennebaud, Gaël; Barla, Pascal; Granier, Xavier; Holly Rushmeier and Oliver Deussen
    We introduce a new approach for defining continuous non-oriented gradient fields from discrete inputs, a fundamental stage for a variety of computer graphics applications such as surface or curve reconstruction, and image stylization. Our approach builds on a moving least square formalism that computes higher‐order local approximations of non‐oriented input gradients. In particular, we show that our novel isotropic linear approximation outperforms its lower‐order alternative: surface or image structures are much better preserved, and instabilities are significantly reduced. Thanks to its ease of implementation (on both CPU and GPU) and small performance overhead, we believe our approach will find a widespread use in graphics applications, as demonstrated by the variety of our results.We introduce a new approach for defining continuous non‐oriented gradient fields from discrete inputs, a fundamental stage for a variety of computer graphics applications such as surface or curve reconstruction, and image stylization. Our approach builds on a moving least square formalism that computes higher‐order local approximations of non‐oriented input gradients. In particular, we show that our novel isotropic linear approximation outperforms its lower‐order alternative: surface or image structures are much better preserved, and instabilities are significantly reduced.