MATLAB Implementation of Hausdorff Distance Algorithm

We present a MATLAB-implemented Hausdorff distance program designed for comparing distances between two shapes. The Hausdorff distance metric provides superior precision in measuring shape dissimilarity compared to conventional distance metrics. This implementation employs mathematical formulas to compute the Hausdorff distance through efficient point-set comparisons, ensuring high reliability and computational accuracy. The program's core algorithm calculates the bidirectional Hausdorff distance by first computing the minimum distances from each point in set A to set B, and vice versa, then selecting the maximum value among these minimum distances. Key MATLAB functions utilized include pdist2 for efficient point-to-point distance calculations and max/min operations for determining the supremum and infimum distances. This tool finds practical applications in medical image analysis for comparative studies and in machine learning for shape classification tasks. The modular code structure allows for straightforward optimization and customization to accommodate specific requirements, such as handling different data formats or implementing accelerated computation techniques for large datasets. The implementation supports both directed and undirected Hausdorff distance calculations, with optional parameters for controlling computational precision and memory usage.