A new method of estimating wavelet with desired features from a given signal
Signal Processing - Content-based image and video retrieval
Design of matched wavelets based on generalized Mexican-hat function
Signal Processing
A variational method for designing wavelets to match a specified signal
Signal Processing
Wavelet-based multiresolution analysis of ridges for fingerprint liveness detection
International Journal of Information and Computer Security
Image denoising with neighbour dependency and customized wavelet and threshold
Pattern Recognition
Remote pulse wave monitoring system
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Two-channel nonseparable wavelets statistically matched to 2-D images
Signal Processing
On the best evolutionary wavelet based filter to compress a specific signal
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Signal dependent biorthogonal wavelet based representation
ICECS'05 Proceedings of the 4th WSEAS international conference on Electronics, control and signal processing
Data dependent wavelet filtering for lossless image compression
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Adapted wavelets for pattern detection
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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Algorithms for designing a mother wavelet ψ(x) such that it matches a signal of interest and such that the family of wavelets {2-(j2)/ψ(2-jx-k)} forms an orthonormal Riesz basis of L2(ℛ) are developed. The algorithms are based on a closed form solution for finding the scaling function spectrum from the wavelet spectrum. Many applications require wavelets that are matched to a signal of interest. Most current design techniques, however, do not design the wavelet directly. They either build a composite wavelet from a library of previously designed wavelets, modify the bases in an existing multiresolution analysis or design a scaling function that generates a multiresolution analysis with some desired properties. In this paper, two sets of equations are developed that allow us to design the wavelet directly from the signal of interest. Both sets impose bandlimitedness, resulting in closed form solutions. The first set derives expressions for continuous matched wavelet spectrum amplitudes. The second set of equations provides a direct discrete algorithm for calculating close approximations to the optimal complex wavelet spectrum. The discrete solution for the matched wavelet spectrum amplitude is identical to that of the continuous solution at the sampled frequencies. An interesting byproduct of this work is the result that Meyer's spectrum amplitude construction for an orthonormal bandlimited wavelet is not only sufficient but necessary. Specific examples are given which demonstrate the performance of the wavelet matching algorithms for both known orthonormal wavelets and arbitrary signals.