Image Matching Using Hausdorff Distance in MATLAB

In MATLAB, the Hausdorff distance algorithm can be employed to address image matching problems between two images. This algorithm computes the Hausdorff distance between two point sets, representing the maximum distance value between corresponding image features. The implementation typically involves calculating both directional distances (from set A to B and vice versa) and taking the maximum value using functions like pdist2 for point-wise distance computation. Prior to applying the Hausdorff algorithm, images should undergo preprocessing steps such as scaling, rotation correction, or translation alignment to enhance matching accuracy. This may involve using MATLAB's Image Processing Toolbox functions like imresize for scaling or imrotate for orientation adjustments. Alternative algorithms like SIFT (Scale-Invariant Feature Transform) or SURF (Speeded-Up Robust Features) can also be employed for feature-based image matching. SIFT implementation involves keypoint detection using difference-of-Gaussians and descriptor generation via gradient orientation histograms, while SURF utilizes Hessian matrix-based detectors for faster computation. In practical applications, the choice of algorithm should be determined by specific requirements such as computational efficiency, scale invariance, or robustness to transformations. For Hausdorff distance implementation, developers might create custom functions to handle edge cases and optimize distance calculations for large image datasets.