Abstract
The main approaches to texture modeling are the statistical psychophysically inspired model and the patch-based model. In the first model the texture is characterized by a sophisticated statistical signature. The associated sampling algorithm estimates this signature from the example and produces a genuinely different texture. This texture nevertheless often loses accuracy. The second model boils down to a clever copy-paste procedure, which stitches verbatim copies of large regions of the example. We propose in this communication to involve a locally Gaussian texture model in the patch space. It permits to synthesize textures that are everywhere different from the original but with better quality than the purely statistical methods.
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
| Publisher | IEEE |
| Pages | 4667-4671 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479957514 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Gaussian Modeling
- Image Patches
- Texture Synthesis