Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
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
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Texture Mixing and Texture Movie Synthesis Using Statistical Learning
IEEE Transactions on Visualization and Computer Graphics
Image replacement through texture synthesis
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
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
Synthesis of progressively-variant textures on arbitrary surfaces
ACM SIGGRAPH 2003 Papers
Fragment-based image completion
ACM SIGGRAPH 2003 Papers
A hybrid-based texture synthesis approach
The Visual Computer: International Journal of Computer Graphics
Jump map-based interactive texture synthesis
ACM Transactions on Graphics (TOG)
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Generating Sub-Resolution Detail in Images and Volumes Using Constrained Texture Synthesis
VIS '04 Proceedings of the conference on Visualization '04
Texture design using a simplicial complex of morphable textures
ACM SIGGRAPH 2005 Papers
Fast block-based image restoration employing the improved best neighborhood matching approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We present a method of synthesising a texture using dynamic neighbourhood matching. Like existing pixel-based methods, the output is synthesised by comparing neighbourhoods of sample and target pixels. However unlike most methods, we do not specify a fixed neighbourhood size a priori. Instead the window size is determined dynamically for each pixel that is synthesised. Typically the output image is initialised with noise. In our approach, we use patches as a seed with which to guide the matching process. The patches are retrieved using the watershed method to isolate texture elements. As a result our outputs have reduced smudging, and fewer jagged drawn out texture anomalies that are problematic in existing pixel-based methods.