Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
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)
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Bayesian Rendering with Non-Parametric Multiscale Prior Model
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
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
Feature matching and deformation for texture synthesis
ACM SIGGRAPH 2004 Papers
Separating Style and Content with Bilinear Models
Neural Computation
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Artistic images and paintings can be regarded as a composition of content and style. The aim of artistic style transfer is to synthesize a stylized image with content of source image but novel style from style example. Based on texture synthesis, a novel Feature Guided Texture Synthesis (FGTS) algorithm for artistic style transfer is proposed in this paper. Compared with existing example-based methods, the content of a source image is better defined in FGTS with a feature field generated from the source image. Though the style is modelled with low-level statistical features, the style transfer process is guided with the feature field which incorporates style and content during synthesis process. Moreover, a modified L neighborhood distance metric is developed to provide better measures of perceptual similarity. Results and comparisons are given to demonstrate that FGTS is an effective method for artistic style transfer.