MULTISCALE EDGE DETECTION USING WAVELET TRANSFORM COMPARED TO OTHER METHODOLOGIES

This work presents the properties of multiscale edges through the wavelet theory. For pattern recognition, one often needs to discriminate different types of edges. Wavelets are mathematical functions that cut up data into different frequency components, and study each component with a resolution matched to its scale. Wavelets are an extremely useful tool for coding images and other real signals; because the wavelet transform is local in both time (space) and frequency, it localizes information very well compared to other transforms. It is shown that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures. Numerical descriptors of edge types are derived. The completeness of a multiscale edge representation is also studied. The wavelet transformation has been proved to be a very promising technique for the multiscale edge detection applied both to 1-D and 2-D signals. The dyadic wavelet transforms at two adjacent scales are multiplied as a product function to magnify the edge structures and suppress the noise. One determined the edges as the local maxima directly in the scale product after an efficient thresholding. One showed that the classical edge detectors work fine with high-quality pictures, but often are not good enough for noisy pictures because they cannot distinguish edges of different significance. This work also deals with the edge detection of noisy images and the optimization of the wavelets for edge detection.The dyadic wavelet transforms at two adjacent scales are multiplied as a product function to magnify the edge structures and suppress the noise.In this work one has developed an algorithm for the multiscale wavelet edge detection and compared between the output from obtaining canny edge detector to theimages and the results are tabulated.Finally one discussed the effect of adding noise or blurred to the image, and applied that to the proposed model and other classical edge detectors.(We take canny, sobel as an example), Finally, it is concluded that the proposed model gives a better edge detection (High number of edge points), The lower the scale the higher number of edge points.</p

Wavelet Based Analysis of Cosmic Gamma-Ray Burst Time Series

Multiscale edge detection using wavelet transform maxima provides a robust method to compress information in a transient signal. We apply this method to Gamma-Ray Burst (GRB) time series data from the Compton Gamma Ray Observatory (CGRO). This provides a method to quantify the variability, identify structures, significantly suppress noise and compress the volume of data by as much as a factor of 10. 1 Multiscale Edge Detection We explore the use of new tools for the study of gamma-ray burst (GRB) lightcurves. GRBs are transient bursts of gamma rays observed from uniformly distributed directions in the sky. Little is known about their origin. In addition to GRB&apos;s isotropy, the energy spectrum of GRBs lacks interesting features such as emission or absorption lines. The temporal observations of GRBs (lightcurves) promise to be enlightening because they contain the dynamics of the bursts. GRB lightcurves are nonstationary time series. Consequently, traditional time series analysis tools (..