the s-transform has widely been used in the analysis of non-stationary time series. a simple method to obtain depth estimates of gravity field sources is introduced in this study. we have developed a new method based on the spectral characteristics of downward continuation to estimate depth of structures. this calculation procedure is based on replacement of the fourier transform with the s-tra...

Color image enhancement is a very important pre-processing stage in face detection and face recognition applications especially when the environment is very dark. In this paper, a color color image enhancement with the adaptive filter and discrete wavelet transform (DWT) is proposed. Discrete wavelet transform is used to improve image enhancement and stationary wavelet transform is used to redu...

The use of watersheds in image segmentation relies mostly on a good estimation of image gradients. However, background noise tends to produce spurious gradients, causing over-segmentation and degrading the result of the watershed transform. Also, low-contrast edges generate small magnitude gradients, causing distinct regions to be erroneously merged. In this paper, a new technique is presented ...

A new method for analysing nonlinear and non-stationary data has recently been developed, namely the partial-differential-equation (PDE) transform. The PDE transform is based on the use of coupled arbitrarily high order PDEs to decompose signals and images into various functional frequency modes, which allow perfect reconstruction of the original signals and images. The PDE transform is compare...

This paper describes a technique for noise reduction in synthetic aperture radar interferometry. The noisy interferogram is decomposed using undecimated wavelet transform and the coefficients are weighted. A novel method for computing the weights for each subband, based on an estimate of the relative noise content in them, is presented with a median filter used as the noise estimator. The propo...

The Empirical Mode Decomposition (EMD) is a popular algorithm used for the processing of non-linear and non-stationary signals. In this paper we implemented the EMD algorithm and study the use of EMD in broad range of applications for extracting data from the signals. It decomposes the signal into highly varying Intrinsic Mode Functions (IMF) and slowly varying residues. Finally the results are...

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific re...

A simple preprocessing method for extracting boundary regions of moving objects in a video sequence is presented. We use Chui’s overssampled shift-invariant wavelet transform and the multiresolution motion estimation and compensation in the wavelet domain. Dominant prediction errors often appear along the boundary of a moving object. Our algorithm is developed to detect boundary regions at a co...

A new rough-wavelet granular space based model for land cover classification of multispectral remote sensing image, is described in the present article. In this model, we propose the formulation of classdependent (CD) granules in wavelet domain using shift-invariant wavelet transform (WT). Shift-invariant WT is carried out with properly selected wavelet base and decomposition level(s). The tran...

In this paper, a technique is presented that incorporates an irregular triangle mesh into wavelet-domain motion-estimation and motion-compensation using a shift-invariant redundant wavelet transform. Triangle vertices are identified by a simple correlation operator locating image edges in the wavelet subbands, while motion compensation takes place through an affine transformation mapping triang...