Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
What is the goal of sensory coding?
Neural Computation
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Matrix computations (3rd ed.)
Sparse coding in the primate cortex
The handbook of brain theory and neural networks
A unifying review of linear Gaussian models
Neural Computation
Neural Computation
Stochastic models for generic images
Quarterly of Applied Mathematics
Digital Pictures: Representation and Compression
Digital Pictures: Representation and Compression
Estimating Overcomplete Independent Component Bases for Image Windows
Journal of Mathematical Imaging and Vision
Mean-field approaches to independent component analysis
Neural Computation
Statistical Modeling of Texture Sketch
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Fast Atomic Decomposition by the Inhibition Method
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Topographic Independent Component Analysis
Neural Computation
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Optimal subset selection for adaptive signal representation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Matching pursuit filters applied to face identification
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Very low bit-rate video coding based on matching pursuits
IEEE Transactions on Circuits and Systems for Video Technology
Estimating Overcomplete Independent Component Bases for Image Windows
Journal of Mathematical Imaging and Vision
Primal sketch: Integrating structure and texture
Computer Vision and Image Understanding
On the computational rationale for generative models
Computer Vision and Image Understanding
Sparse approximation of images inspired from the functional architecture of the primary visual areas
EURASIP Journal on Applied Signal Processing
Contour tracking based on marginalized likelihood ratios
Image and Vision Computing
Role of homeostasis in learning sparse representations
Neural Computation
An easily computable eight times overcomplete ICA method for image data
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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Linear expansions of images find many applications in image processing and computer vision. Overcomplete expansions are often desirable, as they are better models of the image-generation process. Such expansions lead to the use of sparse codes. However, minimizing the number of non-zero coefficients of linear expansions is an unsolved problem. In this article, a generative-model framework is used to analyze the requirements, the difficulty, and current approaches to sparse image coding.