Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multispectral Random Field Models for Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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)
Synthesis of bidirectional texture functions on arbitrary surfaces
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Towards real-time texture synthesis with the jump map
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
A Multiresolution Causal Colour Texture Model
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Extreme Compression and Modeling of Bidirectional Texture Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A new generative multispectral texture model based on discrete distribution mixtures is introduced. Statistical texture properties are represented by a discrete distribution mixture of product components. A natural colour or multispectral texture is spectrally factorized and discrete mixtures models are learned and used to synthesize single orthogonal monospectral components. Texture synthesis is based on easy computation of arbitrary conditional distributions from the model. Finally single synthesized monospectral texture components are transformed into the required synthetic colour texture. This model can easily serve for texture segmentation, retrieval or to model single factors in complex Bidirectional Texture Function (BTF) space models. The advantages and weak points of the presented approach are demonstrated on several colour texture applications.