Optimized near real time nearest neighbor search algorithm for different class points association

Abstract EN

A new technique is proposed to carry out the nearest neighbor (NN) association in a large set of different class points in an automated, optimized, and speedy manner.

The algorithm makes use of the K-dimensions tree to, mutually, organize the examined set of points and initiate the different stages of NN search algorithm.

Our algorithm assumes no prior knowledge about the spatial distribution of the examined set of points, which means it has the potential to be applied to many applications in signal processing, wireless communications modeling, image processing, computer vision, biochemical, and feature extraction.

It will be very useful for many applications that require real-time output.

Our simulations show that we can get an optimal solution of associations while saving more than 80% of the processing time when compared with the exhaustive sorted list-based search method.