Ten lectures on wavelets
Atomic Decomposition by Basis Pursuit
SIAM Review
Matching pursuit filters applied to face identification
IEEE Transactions on Image Processing
Very low bit-rate video coding based on matching pursuits
IEEE Transactions on Circuits and Systems for Video Technology
EURASIP Journal on Applied Signal Processing
Conditional spectral moments in matching pursuit based on the chirplet elementary function
Digital Signal Processing
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Resolution Improvement of Scanning Acoustic Microscopy Using Sparse Signal Representation
Journal of Signal Processing Systems
On the statistics of matching pursuit angles
Signal Processing
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In digital signal processing it is often advantageous to analyze a given signal using an adaptive method. The signal is approximated or represented as a superposition of "basic" waveforms chosen from a dictionary of such waveforms so as to best match the signal. The matching pursuit algorithm of Mallat and Zhang is such a method and is discussed in the context of discretized Gabor functions on an interval. We describe two software implementations based on these dictionaries. Both implementations rely on functions defined on an interval to avoid edge effects. One implementation allows for users to have great flexibility in the Gabor dictionary to be used. This is a useful improvement over other implementations, which only allow for a fixed dictionary. The other implementation takes advantage of the FFT algorithm and is faster. These implementations are written in C++, and can be used in practical applications.