ABSTRACT
This chapter provides a comprehensive glance of different methods used to characterize the time series evolution of the dynamic speckle patterns within the paradigm of frequency and time-frequency analysis. In most practical applications, discrete-time signals are obtained by sampling continuous-time signals periodically in time. There are several types of filters depending on the characteristic of the spectral (magnitude and phase) response. The decomposition process can be iterated with successive approximations being decomposed in turn, so that one signal is broken down into many lower resolution components. The discrete wavelet transform makes no assumptions about signal stationary feature. The dynamic speckle patterns corresponding to different drying stages in samples of synthetic paint were registered after extending them on a glass surface by means of an extender. The extraction of a set of texture feature vectors using the 2-D wavelet transform to characterize the time evolution of dynamic speckle patterns was proposed by Fernandez Limia.
