Real-time texture synthesis by patch-based sampling
ACM Transactions on Graphics (TOG)
Self-similarity based texture editing
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Texture Mixing and Texture Movie Synthesis Using Statistical Learning
IEEE Transactions on Visualization and Computer Graphics
A Multiresolution Causal Colour Texture Model
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A Multiscale Colour Texture Model
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Interactive Modeling of Tree Bark
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
Integrating procedural textures with replicated image editing
GRAPHITE '05 Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
ACM SIGGRAPH 2006 Papers
Extreme Compression and Modeling of Bidirectional Texture Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Editing Using Frequency Swap Strategy
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Bidirectional Texture Function Modeling: A State of the Art Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Compound Gauss-Markov random fields for image estimation
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian multichannel image restoration using compound Gauss-Markov random fields
IEEE Transactions on Image Processing
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
This paper describes high visual quality compound Markov random field texture model capable to realistically model multispectral bidirectional texture function, which is currently the most advanced representation of visual properties of surface materials. The presented compound Markov random field model combines a non-parametric control random field with analytically solvable wide-sense Markov representation for single regions and thus allows very efficient non-iterative parameters estimation as well as the compound random field synthesis. The compound Markov random field model is utilized for realistic texture compression, enlargement, and powerful automatic texture editing. Edited textures maintain their original layout but adopt anticipated local characteristics from one or several parent target textures.