Title: | Procedural texture synthesis by locally controlled spot noise |
Authors: | Pavie, Nicolas Gilet, Guillaume Dischler, Jean-Michel Ghazanfarpour, Djamchid |
Citation: | WSCG 2016: full papers proceedings: 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS Association, p. 71-79. |
Issue Date: | 2016 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | wscg.zcu.cz/WSCG2016/!!_CSRN-2601.pdf http://hdl.handle.net/11025/29533 |
ISBN: | 978-80-86943-57-2 |
ISSN: | 2464–4617 (print) 2464–4625 (CD-ROM) |
Keywords: | procedurální texturace;syntéza obrazu;procesní hluk |
Keywords in different language: | procedural texturing;image synthesis;procedural noise |
Abstract: | Procedural noises based on power spectrum definition and random phases have been widely used for procedural texturing, but using a noise process with random phases limits the types of possible patterns to Gaussian patterns (i.e. irregular textures with no structural features). Local Random Phase (LRP) Noise has introduced control over structural features in a noise model by fixing the frequencies and phase information of desired features, but this approach requires storing these frequencies. Space distortion and randomization must also be used to avoid repetitions and periodicity. In this paper, we present a noise model based on non-uniform random distributions of multiple Gaussian functions for synthesizing semi-structured textures. We extend the LRP noise model by using a spot noise based on a controlled distribution of kernels (spots), as an alternative formulation to local noises aligned on a regular grid. Spots are created as a combination of Gaussian functions to match either a specific power spectrum or a user-defined texture element. Our noise model improves the control over local structural features while keeping the benefits of LRP noise. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG 2016: Full Papers Proceedings |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/29533
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