Various video processing applications, such as tracking, requires low complexity and reliable segmentation of objects. Global motion and background clutter often acts as key constraints to perform reliable segmentation. In this paper, we propose a video segmentation algorithm for tracking application that handles these constraints by operating on high and low frequency wavelet bands simultaneou...

Panpan Zhang In this paper, I am going to introduce statistical self-similarity for discrete time series. My thesis is divided into three parts: In the first part, I will give a mathematical definition of self-similarity and detect the main properties of self-similar processes. At the end of this part, fractional Brownian motion(fBm) will be used as an example to check these properties. In the ...

Time-varying spectra of non-stationary time-series commonly used are spectrograms from the Short-Time Fourier Transform (STFT). The most prominent limitation of the Fourier Transform is that of frequency resolution. To overcome the limitation the Wavelet Transform, Wigner-Ville Distribution and the Min-Norm subspace method have been applied for spectrum estimation of non-stationary signals caus...

In this correspondence I propose an image super-resolution technique based on interpolation of high frequency sub-band images obtained by Discrete Wavelet Transform on input image. In those sub-bands edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. The wavelet transform is applied in order to decompose image into different subbands. In those sub-ban...

Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the fe...

We introduce a wavelet-based model of local stationarity. This model enlarges the class of locally stationary wavelet processes and contains processes whose spectral density function may change very suddenly in time. A notion of time-varying wavelet spectrum is uniquely defined as a wavelet-type transform of the autocovariance function with respect to so-called autocorrelation wavelets. This le...

The downsampling of a discrete wavelet transform (DWT) has a side effect of the lack of shiftinvariance. There are two main solutions for this effect: one is the stationary wavelet transform (SWT), which does not apply downsampling. The other is the complex DWT (CDWT), which uses dual multiresolution analysis (MRA). We choose the CDWT as a target of research. It is well known that wavelet funct...

The properties of Wavelet Transform can be successfully applied for analysis and processing of non stationary signals e.g., speech and image processing, data compression and communications. Due to the growing number of applications in various areas, it is necessary to explore the hardware implementation options of the Discrete Wavelet Transform (DWT). The Wavelet Series is just a sampled versio...

It is very important to capture distant-talking speech with high quality for teleconferencing systems or voice-controlled systems. For this purpose, microphone array steering and Fourier spectral subtraction, for example, are ideal candidates. A combination technique using both microphone array steering and Fourier spectral subtraction has also been proposed to improve performance. However, it ...

In this work, we have developed an efficient approach for enhancing speech by combining tracking of non stationary noise algorithm and Continues Wavelet Transform (CWT). Tracking of non stationary noise method that is based on data-driven recursive noise power estimation was proposed by Jan S. Erkelens and Richard Heusdens. The Continues Wavelet decomposition of speech signal uses adaptive leve...