A multiresolution spline with application to image mosaics
ACM Transactions on Graphics (TOG)
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Creating full view panoramic image mosaics and environment maps
Proceedings of the 24th 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
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
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)
Texture Recognition Using a Non-Parametric Multi-Scale Statistical Model
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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We consider texture modeling techniques which are exemplar-based, i.e. techniques which use several example texture images to learn pixel arrangements. The motivation for these techniques is that the structure of a texture can be characterized by the spatial distribution of pixels in a neighborhood of the image or the multiresolution pyramid. To get another instance of the same type of texture, one can rearrange the pixels as long as certain spatial neighbor relationships are enforced. In this work, we investigate two components of exemplar-based modeling: (1) grouping examples for computational savings during analysis and (2) blending for artifact removal during synthesis. First, we employ clustering in order to group example features and this method provides a significant computational savings without compromising the quality of the texture characterization. Second, we implement techniques for blending to remove border artifacts during the placement stage of texture synthesis. We show that for many textures, the pixel rearrangements can be done without filtering or neighborhood constraints, as long as the blending of borders is done well. Specifically, random rearrangement of texture patches generates a new texture instance of the same texture type when artifacts are removed with blending. We investigate existing blending approaches and introduce a blending method based on image fusion.