A new algorithm is proposed which finds the Nearest Neighbour of a given sample in
approximately constant average time complexity (ie independent of the data set size). The …

In this paper we show how we can take advantage of using different distance functions in
image processing applications. The proposed methods are based on well-known algorithms …

A distance transformation converts a binary digital image, consisting of feature and non-
feature pixels, into an image where all non-feature pixels have a value corresponding to the …

Determining, or selecting a distance measure over the input feature space is a fundamental
problem in pattern recognition. A notable metric, called the image euclidean distance (IMED) …

On the Distance Transform of Binary Images Page 1 On the Distance Transform of Binary
Images Muhammad H. Alsuwaiyel Department of Information and Computer Science King Fahd …

A unified treatment is presented for determining the distance between two points in a two-or
three-dimensional digitized space. Instead of a global Euclidean distance, a distance …

Distance metric is widely used in similarity estimation. In this paper we find that the most
popular Euclidean and Manhattan distance may not be suitable for all data distributions. A …

In the process of designing pattern recognition systems one may choose a representation
based on pairwise dissimilarities between objects. This is especially appealing when a set …

We will present a new approach for pattern matching which is applicable to very high
dimensional features. This approach is based on maximizing a novel non-linear measure of" …

The problem considered in this article is how to solve the image correspondence problem in
cases where it is important to measure changes in the contour, position, and spatial …