This method regards the problem of watermark detection as a blind source separation problem, and thus uses independent component analysis to estimate the embedded watermark.
The latter contains 16 chapters by 43 international academics and researchers on advanced topics of image processing in remote sensing including image modeling, statistical image classifiers, change detection, independent component analysis, vertex component analysis, image fusion for better classification or segmentation, 2-D time series modeling, neural network classifications, and accuracy assessment and information-theoretic measure of multiband images.
Features * Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis * Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems * Examples and applications in signal and information extraction from noisy data * Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models Advanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis.
For analysis such as data mining and feature extraction in applications including biomedical, mechanical and seismological signals, engineers and scientists can use the independent component analysis and principal component analysis tools as part of the new time-series analysis functionality.
EEGLAB incorporates a graphic user interface (GUI) for analyzing high-density electroencephalographic (EEG) or 'brainwave' data combining two new types of EEG analysis, independent component analysis (ICA) and time/frequency analysis, that expand the amount of information researchers can extract from the electrical brain data they record during EEG experiments.
