Edmond Chalom’s research while affiliated with Jerusalem College of Technology and other places

Why is measuring image similarity useful? There are abundant computer imaging applications requiring some kind of similarity measurement as part of their processes. Although the applications are quite varied, and the implementation details of each solution are unique, all share the common thread in that features or attributes of the image (in each specific application) are measured and then compared to other features from a database of images or with some reference model to extract some meaningful conclusions or functionality about the image data on hand. This paper describes several methods of measuring image similarity: a pattern recognition approach, comparison of frames in a video sequence, image stabilization using a homographic transformation, and using image feature points to compute similarities and generate an image mosaic.

Fourier analysis is often thought of as a technique for measuring the spectral content of a signal over the period in which the signal was measured. In this column, we discuss techniques that allow you to use Fourier analysis to provide accurate measurements of the instantaneous frequency of a signal. We discuss the short-time Fourier transform and its main problem; we explain why it gives a very coarse estimate of the frequency. We show how it is possible to use the phase information provided by the discrete Fourier transform (DFT) to produce a much sharper estimate of the frequency. Finally, we show how the improved estimate can be used to build a virtual FM demodulator.