Mathematical Methods for Signal and Image Analysis and Representation presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold (typically, but not necessarily, a subset of spacetime). The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies. Furthermore, it does not focus on specific applications, although some are detailed for the sake of illustration, but on the methodological frameworks on which such applications are built, making it an ideal companion for those seeking a rigorous methodological basis for specific algorithms as well as for those interested in the fundamental methodology per se. Covering many topics at the forefront of current research, including anisotropic diffusion filtering of tensor fields, this book will be of particular interest to graduate and postgraduate students and researchers in the fields of computer vision, medical imaging and visual perception.
Wu, Z. and Huang, N. E. Ensemble empirical mode decomposition: A noise-assisted data analysis method, Advances in Adaptive Data Analysis, 1(1), World Scientific, Singapore, 1-41, 2009. . Wang, G., Chen, X.-Y., Qiao, F.-L., Wu, Z., ...
Signal Processing: A Mathematical Approach is designed to show how many of the mathematical tools the reader knows can be used to understand and employ signal processing techniques in an applied environment.
With a balanced focus on mathematical theory and computational techniques, this self-contained book equips readers with the essential knowledge needed to transition smoothly from mathematical models to practical digital data applications.
This contributed volume explores the connection between the theoretical aspects of harmonic analysis and the construction of advanced multiscale representations that have emerged in signal and image processing.
Digital image processing technology has developed markedly over the last ten years, and more and more information is being conveyed through its display and analysis. The way in which image...
Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with...
Stein, E. M., Singular Integrals and Differentiability Properties of Functions, Princeton Univ. Press, Princeton, NJ, 1970. Stein, E. M., Harmonic Analysis, Real Variable Methods, Orthogonality, and Oscillation Integrals, ...
The algorithm is completely automatic and the encoding/decoding operations are implemented in a fast way. References 17. ... A.Z. Averbuch, F. Meyer, J.-O. Stromberg, R. Coifman, A. Vassiliou, Efficient compression for seismic data.
D, 60:259-268, 1992. A. Said and W. Pearlman, A new, fast, and efficient image codec based on set partitioning in hierarchial trees, IEEE Trans. on Circuits Systems for Video Technology, 6(3):243–250, June 1996.
In this book, the authors explain how to process each projection by a system of linear equations, or linear convolutions, to calculate the corresponding part of the 2-D tensor or paired transform of the discrete image.