In this paper, we present comparative analysis of scale-invariant feature extraction using different wavelet bases. The main advantage of the wavelet transform is the multi-resolution analysis. Furthermore, wavelets enable localization in both space and frequency domains and high-frequency salient feature detection. Wavelet transforms can use various basis functions. This research aims at compa...

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 ...

Human has a duty to preserve the nature, preserving the plant is one of the examples. This research emphasis on ornamental plant that has functionality not only as ornament plant but also as a medicinal plant. Purpose of this research is to find the best of the particular feature extraction components from several wavelet transformations. It consists of Daubechies, Dyadic, and Dualtree complex ...

We consider two types of Besov spaces on the closed snowflake, defined by traces and with the help of the homeomorphic map from the interval [0, 3]. We compare these spaces and characterize them in terms of Daubechies wavelets.

-In this paper, we carry out a comparative study of the efficacy of wavelets belonging to Daubechies and Coiflet family in achieving image segmentation through a fast statistical algorithm.The fact that wavelets belonging to Daubechies family optimally capture the polynomial trends and those of Coiflet family satisfy mini-max condition, makes this comparison interesting. In the context of the p...

Recently the Wavelet Transform has gained a lot of popularity in the field of signal and image processing. This is due to its capability of providing both time and frequency information simultaneously, hence giving a timefrequency representation of the signal. The traditional Fourier Transform can only provide spectral information about a signal. Moreover, the Fourier method only works for stat...

Speech signal analysis is one of the important areas of research in multimedia applications. Discrete Wavelet technique is effectively reduces the unwanted higher or lower order frequency components in a speech signal. Wavelet-based algorithm for audio de-noising is worked out. We focused on audio signals corrupted with white Gaussian noise which is especially hard to remove because it is locat...

The wavelet transform is one of the important methods that are used to minimize noises and to analyze signals. The choice of wavelet and its associated scaling function are very important to obtain the most useful wavelet transforms. In the present work, it was investigated that the easy obtaining of the P and S phases by minimizing the noise at the three-component seismograms displacement reco...

In this paper we propose a method for the detection of heart diseases from electrocardiogram (ECG) signal using wavelet transform technique. Initially the signal is denoised using symlet wavelet with soft thresholding. First we have developed an algorithm for R-peak detection in detailed signal using Harr wavelet. After 4 level decomposition of the ECG signal, using a window of 500 samples maxi...