Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Real-time texture synthesis by patch-based sampling
ACM Transactions on Graphics (TOG)
Synthesis of bidirectional texture functions on arbitrary surfaces
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
Synthesis of progressively-variant textures on arbitrary surfaces
ACM SIGGRAPH 2003 Papers
Feature matching and deformation for texture synthesis
ACM SIGGRAPH 2004 Papers
Parallel controllable texture synthesis
ACM SIGGRAPH 2005 Papers
Texture optimization for example-based synthesis
ACM SIGGRAPH 2005 Papers
Appearance-space texture synthesis
ACM SIGGRAPH 2006 Papers
Fast example-based surface texture synthesis via discrete optimization
The Visual Computer: International Journal of Computer Graphics
Constrained Texture Synthesis via Energy Minimization
IEEE Transactions on Visualization and Computer Graphics
Symmetry-guided texture synthesis and manipulation
ACM Transactions on Graphics (TOG)
An improved image analogy method based on adaptive CUDA-accelerated neighborhood matching framework
The Visual Computer: International Journal of Computer Graphics - CGI'2012 Conference
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The framework of Texture-by-numbers (TBN) synthesizes images of global-varying patterns with intuitive user control. Previous TBN synthesis methods have difficulties in achieving high-quality synthesis results and efficiency simultaneously. This paper proposes a fast TBN synthesis method based on texture optimization, which uses global optimization to solve the controllable non-homogeneous texture synthesis problem. Our algorithm produces high quality synthesis results by combining texture optimization into TBN framework with two improvements. The initialization process is adopted to generate the initial output of the global optimization algorithm, which speeds up the algorithm's convergence rate and ensures synthesis quality. Besides different metrics to measure image similarity are defined to match human visual perception better. To further improve the synthesis speed, the algorithm is entirely implemented on GPU based on CUDA architecture. The experimental results show that this method synthesizes realistic images with high efficiency, which is not only applicable to the traditional TBN application, but also suitable for other applications including non- photorealistic rendering and image in-painting.