International Journal of Wavelets, Multiresolution and Information Processing

In this paper we propose a robust algorithm that solves two related problems: (1) Classification of acoustic signals emitted by different moving vehicles. The recorded signals have to be identified to which pre-existing group they belong to independently of the recording surrounding conditions. (2) Detection of the presence of a vehicle in a certain class via analysis of its acoustic signature against the existing database of recorded and processed acoustic signals. To achieve this detection with minimal false alarms we construct the acoustic signature of a certain vehicle using the distribution of the energies among blocks which consist of coefficients of multiscale local cosine transform (LCT) applied in the frequency domain of the acoustic signal. The proposed algorithm is robust even under severe noise and diverse rough surrounding conditions. This is a generic technology, which has many algorithmic variations, can be used to solve wide range of classification and detection problems which are based on a unique derivation of signatures.

Recently we developed a subdivision scheme for Powell–Sabin splines. It is a triadic scheme and it is general in the sense that it is not restricted to uniform triangles, the vertices must not have valence six and there are no restrictions on the initial triangulation. A sequence of nested spaces or multiresolution analysis can be associated with the base triangulation. In this paper we use the lifting scheme to construct basis functions for the complement space that captures the details that are lost when going to a coarser resolution. The subdivision scheme appears as the first lifting step or prediction step. A second lifting step, the update, is used to achieve certain properties for the complement spaces and the wavelet functions such as orthogonality and vanishing moments. The design of the update step is based on stability considerations. We prove stability for both the scaling functions and the wavelet functions.

We present a method for the detection of pertinent signal features masked by other features with similar spectral content but which perturb (but not necessarily periodically) other constituent parts of the signal. This modulation may be a modulation in frequency and/or amplitude of a locus of chosen selected points on a transform surface. A secondary transform is then performed on this derived signal. We apply the method, which we have termed secondary wavelet feature decoupling (SWFD), to the specific problem of decomposition and analysis of signals used in pulse oximetry where perturbations of the ridges in wavelet space associated with the pulse band may be used to detect respiration features. The method has wide application to problematic signals in many other disciplines.

Most of the content-based image retrieval systems require a distance computation of feature vectors for each candidate image in the image database. This exhaustive search is highly time-consuming and inefficient. This limits the usefulness of such system. Thus there is a growing need for a fast image retrieval system. Multiresolution data-structure algorithm provides a good solution to the above problem. In this paper we propose a wavelet-based multiresolution data-structure algorithm. Wavelet-based multiresolution data-structure further reduce the number of computation by around 50%. In the proposed approach we reuse the information obtained at lower resolution levels to calculate the distance at a higher resolution level. Apart from this, the proposed structure saves memory overheads by about 50% over multiresolution data-structure algorithm. The proposed algorithm can be easily combined with other algorithms for performance enhancement.⁴ In this paper we use the proposed technique to match luminance histogram for image retrieval. Fuzzy histograms enhances performance by considering the similarity between neighboring bins. We have extended the proposed approach to fuzzy histograms for better performance.

With the fast evolution of Multimedia systems, Image compression algorithms are very much needed to achieve effective transmission and compact storage by removing the redundant information of the image data. Wavelet transforms have received significant attention, recently, due to their suitability for a number of important signal and image compression applications and the lapped nature of this transform and the computational simplicity, which comes in the form of filter bank implementations. In this paper, the implementation of image compression algorithms based on discrete wavelet transform such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees coder without lists (SPIHT — No List) and packetizable zero tree wavelet (PZW) coder in DSP processor is dealt in detail and their performance analysis is carried out in terms of different compression ratios, execution timing and for different packet losses. PSNR is used as the criteria for the measurement of reconstructed image quality.

In this paper, image fusion method based on a new class of wavelet — non-separable wavelet with compactly supported, linear phase, orthogonal and dilation matrix is presented. We first construct a non-separable wavelet filter bank. Using these filters, the images involved are decomposed into wavelet pyramids. Then the following fusion algorithm was proposed: for low-frequency part, the average value is selected for new pixel value, For the three high-frequency parts of each level, the standard deviation of each image patch over 3×3 window in the high-frequency sub-images is computed as activity measurement. If the standard deviation of the area 3×3 window is bigger than the standard deviation of the corresponding 3×3 window in the other high-frequency sub-image. The center pixel values of the area window that the weighted area energy is bigger are selected. Otherwise the weighted value of the pixel is computed. Then a new fused image is reconstructed. The performance of the method is evaluated using the entropy, cross-entropy, fusion symmetry, root mean square error and peak-to-peak signal-to-noise ratio. The experiment results show that the non-separable wavelet fusion method proposed in this paper is very close to the performance of the Haar separable wavelet fusion method.

This work gives an algorithm that makes up for the many iterations and the loss of some main features encountered in the usual methods of wavelet transform filtering. The wavelet transform technique is merged with the window low-pass method. The results are acceptable.