Hierarchical pattern mapping

Soler, Cyril; Cani, Marie-Paule; Angelidis, Alexis

doi:10.1145/566570.566635

Cited by 67 publications

(19 citation statements)

References 22 publications

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“…In this procedure, we use approximate-nearest-neighbor search (ANN) [34] to accelerate the neighborhood matching operation between each intermediate tile and the input example. Note that the other techniques such as TSVQ [2,35], FFT [36,37] and mixture trees [38] can also be employed here. Figure 5 shows some texture synthesis results using our algorithm.…”

Section: Parameters and Optimizationsmentioning

confidence: 99%

Robust tile-based texture synthesis using artificial immune system

Dong

Zhou

Paul

2008

Neural Comput Applic

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The original publication is avalaible at www.springerlink.comInternational audienceOne significant problem in tile-based texture synthesis is the presence of conspicuous seams in the tiles. The reason is that sample patches employed as primary patterns of the tile set may not be well stitched if carelessly picked. In this paper, we introduce a robust approach that can stably generate an ω-tile set of high quality and pattern diversity. First, an extendable rule is introduced to increase the number of sample patches to vary the patterns in an ω-tile set. Second, in contrast to other concurrent techniques that randomly choose sample patches for tile construction, ours uses artificial immune system (AIS) to select the feasible patches from the input example. This operation ensures the quality of the whole tile set. Experimental results verify the high quality and efficiency of the proposed algorithm

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Section: Parameters and Optimizationsmentioning

confidence: 99%

Robust tile-based texture synthesis using artificial immune system

Dong

Zhou

Paul

2008

Neural Comput Applic

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“…There is a growing interest in the synthesis and relighting of 3D surface textures [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] . Most surface texture synthesis methods are direct extensions of pixel-based 12 ,21,24 or patch-based algorithms 14 ,22 .…”

Section: Intrductionmentioning

confidence: 99%

“…However, sometimes patch-based algorithms introduce local artifacts in the regions of overlap between neighboring patches. These visible artifacts can be reduced by adaptively varying the patch size 14,13 or explicitly repair bad pixels in the overlap region 5,7,13 .…”

Section: Our Approachmentioning

confidence: 99%

3D surface texture synthesis using improved graph cuts

Dong

2007

MIPPR 2007: Pattern Recognition and Computer Vision

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This paper introduces an improved graph cut approach and combines it with commonly used surface texture representations for synthesis. Unlike graph cut which supports iterative refinement for improvement of the patch seams, the improved graph cut method re-synthesis the max error overlap region using k-nearest neighbor method. This paper follows a simple 3D surface texture synthesis framework, which comprises three steps: capture, synthesis and relighting. Firstly, it represents 3D surface textures under varied illumination directions using gradient and albedo maps. Then, it uses the surface representation to synthesis a description of a larger surface. Finally, it renders the surface representation. It proposes an improved graph cut method for the synthesis step, and the proposed method can generate perceptually smooth images of 3D surface texture from small samples. INTRDUCTIONTexture synthesis is widely recognized to be important for many applications in computer graphics, vision, and image processing. Images of real-world surface textures are affected by many properties such as object geometry and surface reflectance 18 . These surface textures are different from 2D still texture as their images can vary dramatically with illumination directions. With few exceptions, previous research into texture synthesis predominantly focuses on 2D textures 1-8 . In particular, Graph Cuts 1 is proved to be able to generate satisfying 2D textures with little seaming effects. However, 2D methods cannot provide the information required for rendering 3D surface textures with changed illumination and viewpoint conditions.There is a growing interest in the synthesis and relighting of 3D surface textures 9-23 . Most surface texture synthesis methods are direct extensions of pixel-based 12 ,21,24 or patch-based algorithms 14 ,22 . Some early approaches applied direct texture synthesis to the 3D surface 21,24 . These approaches used the Markov random field method of texture synthesis 5,7,24 which requires a spatial neighborhood function. Zalesny and Van Gool, in 16,17 present a multi-view texture model which can synthesize new viewpoints. Shum and his colleagues 20 used the CUReT database 18 to develop a method for the generation of bi-directional texture functions (BTFs). In 19 Leung and Malik proposed the use of 3D textons to synthesize new images under arbitrary viewpoints and illuminations.However, most publications on 3D surface texture are based on "image quilting" 4 or so called "smart copying" algorithms, which can produce unnatural seaming effects for surface representations.In this paper, we introduce an improved graph cut approach and combine it with commonly used surface texture representations for synthesis. The improved graph cut method re-synthesize the max error overlap region using k-nearest neighbor method. We follow a 3D surface texture synthesis framework 9 , which comprises three steps: capture, synthesis and relighting. Firstly, 3D surface textures under varied illumination directions are represented by using ...

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“…This problem has been studied heavily, see for instance [Floater 1997;Soler et al 2002;Levy et al 2002;Gorla et al 2002]. Irregular topologies introduce complex problems, to find a parametrization without too much distortion requires much care.…”

Section: Related Workmentioning

confidence: 99%

Image based flow visualization for curved surfaces

Wijk

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control

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A new method for the synthesis of dense, vector-field aligned textures on curved surfaces is presented, called IBFVS. The method is based on Image Based Flow Visualization (IBFV). In IBFV twodimensional animated textures are produced by defining each frame of a flow animation as a blend between a warped version of the previous image and a number of filtered white noise images. We produce flow aligned texture on arbitrary three-dimensional triangle meshes in the same spirit as the original method: Texture is generated directly in image space. We show that IBFVS is efficient and effective. High performance (typically fifty frames or more per second) is achieved by exploiting graphics hardware. Also, IBFVS can easily be implemented and a variety of effects can be achieved. Applications are flow visualization and surface rendering. Specifically, we show how to visualize the wind field on the earth and how to render a dirty bronze bunny.

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Hierarchical pattern mapping

Cited by 67 publications

References 22 publications

Robust tile-based texture synthesis using artificial immune system

Robust tile-based texture synthesis using artificial immune system

3D surface texture synthesis using improved graph cuts

Image based flow visualization for curved surfaces

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