A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

It is now well admitted in the computer vision literature that a multi-resolution decomposition provides a useful image representation for vision algorithms. In this paper we show that the wavelet theory recently developed by the mathematician Y. Meyer enables us to understand and model the concepts of resolution and scale. In computer vision we generally do not want to analyze the images at each resolution level because the information is redundant. After processing the signal at a resolution r0, it is more efficient to analyze only the additional details which are available at a higher resolution rl. We prove that this difference of information can be computed by decomposing the signal on a wavelet orthonormal basis and that it can be efficiently calculated with a pyramid transform. This can also be interpreted as a division of the signal in a set of orientation selective frequency channels. Such a decomposition is particularly well adapted for computer vision applications such as signal coding, texture discrimination, edge detection, matching algorithms and fractal analysis

A comparison of adaptive predictors in sub-band coding

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.Bibliography: leaves 82-85.by Paul Ning.M.S

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design