Applications of sampling theorems in wavelet spaces to multiresolution visualization and data segmentation
Dzu K. Le · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
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.