Markov random field models in computer vision
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Multiresolution sampling procedure for analysis and synthesis of texture images
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Image quilting for texture synthesis and transfer
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)
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Optimizing the parameters for patch-based texture synthesis
Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications
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Patch-based texture synthesis is a method for synthesizing bigger texture from smaller sample patch by patch. This method requires two user defined parameters including patch size and boundary zone which cannot directly evaluated. To obtain optimal parameters, we can analyze texture using Markov Random Field, but it is too expensive to be used with large textures. This paper introduces more efficient method to find optimal parameters. Firstly, we use graph-based image segmentation to extract segments from the sample. Secondly, we choose main feature to be preserved in result. Finally, we calculate optimal parameters based on size and repetition of the segments. Our technique reduces time used to determine the parameters compared to former method and can be used with wide range of textures.