A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
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
A maximum entropy approach to natural language processing
Computational Linguistics
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)
Equivalence of Julesz Ensembles and FRAME Models
International Journal of Computer Vision - Special issue on Genomic Signal Processing
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
The Problem of Sparse Image Coding
Journal of Mathematical Imaging and Vision
Modeling Visual Patterns by Integrating Descriptive and Generative Methods
International Journal of Computer Vision
A Geometric Primitive Extraction Process for Remote Sensing Problems
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Stochastic matching pursuit for Bayesian variable selection
Statistics and Computing
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Recent results on sparse coding and independent component analysis suggest that human vision first represents a visual image by a linear superposition of a relatively small number of localized, elongate, oriented image bases. With this representation, the sketch of an image consists of the locations, orientations, and elongations of the image bases, and the sketch can be visually illustrated by depicting each image base by a linelet of the same length and orientation. Built on the insight of sparse and independent component analysis, we propose a two-level generative model for textures. At the bottom-level, the texture image is represented by a linear superposition of image bases. At the top-level, a Markov model is assumed for the placement of the image bases or the sketch, and the model is characterized by a set of simple geometrical feature statistics.