1 September 1995 Applications of sampling theorems in wavelet spaces to multiresolution visualization and data segmentation
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Abstract
Sampling `theorems' in `wavelet spaces' are very useful for the integration of `wavelet multiresolution' techniques with various time-domain methods for data processing. In this paper, the application potential of `wavelet sampling theorem' will be illustrated through a few examples of dynamical data analysis and filtering. The main results among our recent applications of the wavelet sampling principles for data processing include the `compact- harmonic wavelets' and a new technique for time-frequency analysis. This new analysis technique provides localized wavelet filters with arbitrarily adjustable frequency-resolution, and the exact reconstruction capability. These filter qualities are both useful and essential for the accurate representation of local power-spectra, and segmentation of signals. These results and underlying ideas are also applicable to the fields of imaging and data compression.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dzu K. Le, "Applications of sampling theorems in wavelet spaces to multiresolution visualization and data segmentation", Proc. SPIE 2569, Wavelet Applications in Signal and Image Processing III, (1 September 1995); doi: 10.1117/12.217577; https://doi.org/10.1117/12.217577
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