Nearest Neighbor Search Algorithm Implementation

In this research, we implement the nearest neighbor search algorithm using MATLAB, which efficiently identifies point sets within the k-neighborhood range of specified points. This algorithm plays a crucial role in computer vision and pattern recognition applications. Through MATLAB's kd-tree implementation (using functions like rangesearch or knnsearch), the algorithm achieves efficient spatial partitioning and nearest neighbor queries. The implementation typically involves constructing spatial data structures and applying distance metrics to identify neighboring points within specified radius or count parameters. By utilizing this algorithm, we can achieve more precise target localization and significantly improve computational efficiency. Furthermore, this algorithm can be extended to other domains such as computer graphics and medical image processing, where applications include point cloud processing, feature matching, and spatial analysis. These diverse applications continue to drive the evolution and adoption of nearest neighbor search methodologies.