Similarity-Based Segmentation of Multi-Dimensional Signals

Translation Invariant Approach for Measuring Similarity of Signals

In many signal processing applications, an appropriate measure to compare two signals plays a fundamental role in both implementing the algorithm and evaluating its performance. Several techniques have been introduced in literature as similarity measures. However, the existing measures are often either impractical for some applications or they have unsatisfactory results in some other applicati...

Translation Invariant Approach for Measuring Similarity of Signals

In many signal processing applications, an appropriate measure to compare two signals plays a fundamental role in both implementing the algorithm and evaluating its performance. Several techniques have been introduced in literature as similarity measures. However, the existing measures are often either impractical for some applications or they have unsatisfactory results in some other applicati...

Multi Resolution Fuzzy Segmentation of Satellite Images

Extrapolation of multi-dimensional Fourier signals

There is a well-known method of extrapolation of signals with the finite Fourier spectrum developed by Gershberg and Papoulis. Aizenberg introduced a method which uses the theory of Hardy spaces in complex analysis. In a recent publication, a new extrapolation method for one-dimensional signals was proposed. The method is based on combining the methods of Aizenberg and Gershberg-Papoulis and so...

Fast similarity join for multi-dimensional data

To appear in Information Systems Journal, Elsevier, 2005 The efficient processing of multidimensional similarity joins is important for a large class of applications. The dimensionality of the data for these applications ranges from low to high. Most existing methods have focused on the execution of high-dimensional joins over large amounts of disk-based data. The increasing sizes of main memor...