Optimized tile-based texture synthesis

Dong, Weiming; Zhou, Ning; Paul, Jean-Claude

doi:10.1145/1268517.1268558

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…Hence, there is a direct connection between corner colors and the synthesized result. Resulting patch borders are then preferably covered by another unique center patch which is merged with the corner patches via an optimization or graph‐cut technique [KSE*03,NWT*05,DZP07].…”

Section: Example‐based Texture Synthesismentioning

confidence: 99%

Semi‐Stochastic Tilings for Example‐Based Texture Synthesis

Schlömer

Deußen

2010

Computer Graphics Forum

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We investigate semi-stochastic tilings based on Wang or corner tiles for the real-time synthesis of example-based textures. In particular, we propose two new tiling approaches: (1) to replace stochastic tilings with pseudo-random tilings based on the Halton low-discrepancy sequence, and (2) to allow the controllable generation of tilings based on a user-provided probability distribution. Our first method prevents local repetition of texture content as common with stochastic approaches and yields better results with smaller sets of utilized tiles. Our second method allows to directly influence the synthesis result which-in combination with an enhanced tile construction method that merges multiple source textures-extends synthesis tasks to globally-varying textures. We show that both methods can be implemented very efficiently in connection with tile-based texture mapping and also present a general rule that allows to significantly reduce resulting tile sets.

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Section: Example‐based Texture Synthesismentioning

confidence: 99%

Semi‐Stochastic Tilings for Example‐Based Texture Synthesis

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2010

Computer Graphics Forum

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“…These tiles can also be tiled together to synthesize large textures. This method is extended on both patterns and qualities in [6]. Lagae and Dutré [18] propose corner tiles which are square tiles with colored corners.…”

Section: Synthesis Textures From Examplesmentioning

confidence: 99%

Review on Recent Patents in Texture Synthesis

Dong¹,

Paul²

2010

CSENG

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Computer graphics applications often use textures to render synthetic images. These textures can be obtained from a variety of sources such as hand-drawn pictures or scanned photographs. Texture synthesis is an alternative way to create textures. Because synthetic textures can be made any size, visual repetition is avoided. The goal of texture synthesis can be stated as follows: given a texture example, synthesize a new texture that, when perceived by a human observer, appears to be generated by the same underlying process. This paper reviews the recent patents on texture synthesis schemes. The key components in a texture synthesis algorithm, such as neighborhood matching, block sampling, anisometric synthesis, etc., are discussed. Then we discuss the applications of texture synthesis in texture magnification and image repairing. This paper also points out future works on this issue.

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“…Recently, efficient GPU-based texture synthesis techniques [14,15] have also been proposed. Another category is to pre-compute correlative tiles [3,7] or similarity sets [25] for runtime references. They usually achieve real-time performance while sacrificing some result qualities.…”

Section: Related Workmentioning

confidence: 99%

Perspective-aware texture analysis and synthesis

2008

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The original publication is available at www.springerlink.comInternational audienceThis paper presents a novel texture synthesis scheme for anisotropic 2D textures based on perspective feature analysis and energy optimization. Given an example texture, the synthesis process starts with analyzing the texel (TEXture ELement) scale variations to obtain the perspective map (scale map). Feature mask and simple user-assisted scale extraction operations including slant and tilt angles assignment and scale value editing are applied. The scale map represents the global variations of the texel scales in the sample texture. Then, we extend 2D texture optimization techniques to synthesize these kinds of perspectively featured textures. The non-parametric texture optimization approach is integrated with histogram matching, which forces the global statics of the texel scale variations of the synthesized texture to match those of the example. We also demonstrate that our method is well-suited for image completion of a perspectively featured texture region in a digital photo

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Optimized tile-based texture synthesis

Cited by 9 publications

References 37 publications

Semi‐Stochastic Tilings for Example‐Based Texture Synthesis

Semi‐Stochastic Tilings for Example‐Based Texture Synthesis

Review on Recent Patents in Texture Synthesis

Perspective-aware texture analysis and synthesis

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