from sampled data. Our method combines both wavelet shrinkage and cosine-packet best-basis selection in a simple and natural way. The resulting algorithm is fast and automatic. The method has an interpretation as a nonlinear, adaptive form of anisotropic timefrequency smoothing. We introduce a new

pretations (position, frequency, and scale), and we have experimented with feature-extraction methods that use best-basis compression for front-end complexity reduction. The method relies heavily on the remarkable orthogonality properties of the new libraries. It is obviously a nonlinear

of the best-basis method, the complexity constrained best-basis algorithm (CCBB) which allows an adaptive approach, has relatively low complexity and is memory saving. Our CCBB algorithm generates iteratively a scarce library of WP bases by extending a given library according to the energy distribution

The best-basis algorithm has gained much importance on textured-based image compression and denoising of signals. In this paper, an architecture for the wavelet-packet based best-basis algorithm for images is proposed. The paper also describes the architecture for best-tree selection from 2D

. In this paper we will demonstrate the analysis of such signals using a specific tremor signal of Mt. Stromboli (Sicily). We used the Best-Basis Algorithm (BBA) in order to compute a spectrogram which is adapted to signal properties on highly different scales. It turns out that the BBA can reveal better fitting

for decomposition have been proposed, including the Method of Frames (MOF), Matching Pursuit (MP), and, for special dictionaries, the Best Orthogonal Basis (BOB). Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having

signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method—the K-SVD algorithm—generalizing the u-means clustering process. K-SVD is an iterative method

To use wavelet packets for lossy data compression, the following issues must be addressed: quantization of the wavelet subbands, allocation of bits to each subband, and best-basis selection. We present an algorithm for wavelet packets that systematically identifies all bit allocations/best-basis

Random Fields (MRF’s), the resulting energy minimization problems were widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods

.7, and 3.3 times more good alignments (“balanced ” score> 0.3) than the next best method (COMPASS), and 1.6, 2.9, and 9.4 times more than PSI-BLAST, at the family, super-family, and fold level. Speed: HHsearch scans a query of 200 residues against 3691 domains in 33s on an AMD64 3GHz PC. This is 10