SIGGRAPH '96 Proceedings of the 23rd 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
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
Real-time texture synthesis by patch-based sampling
ACM Transactions on Graphics (TOG)
Towards real-time texture synthesis with the jump map
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
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
Fragment-based image completion
ACM SIGGRAPH 2003 Papers
Texture synthesis from multiple sources
ACM SIGGRAPH 2003 Sketches & Applications
Fast and High Quality Overlap Repair for Patch-Based Texture Synthesis
CGI '04 Proceedings of the Computer Graphics International
Feature matching and deformation for texture synthesis
ACM SIGGRAPH 2004 Papers
Near-regular texture analysis and manipulation
ACM SIGGRAPH 2004 Papers
IEEE Transactions on Image Processing
Fast directional image completion
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
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We developed a system including two modules: the texture analysis module and the texture synthesis module. The analysis module is capable of analyzing an input image and performing the training process by using this image data. According to the training non-periodic or periodic pattern, we use different sampling methods to have different amount of patches in order to reduce the emergences of the seams of the output synthesized image. In addition, the properties of principal component analysis (PCA) are used to reduce the dimensions of the data representation and to recombine the appearance of the features (i.e. eigenvectors). Then the vector quantization (VQ) algorithm is employed to reduce the time spent on matching comparison. For the synthesis module, the training data is used to synthesize a large output texture, or is employed to replace the removed regions of an image. The multi-resolution approach is applied to accelerate the procedure of our algorithm: the down-sampling step is the training process and the up-sampling step is in the order of reconstructing (or synthesizing) the large removed region without needing to assign initial random values or approximate values. Therefore, our system can rapidly obtain a high image quality and promising result.