Image Template Matching Algorithm Based on Hausdorff Distance

The Hausdorff distance-based image template matching algorithm is a widely used technique in image processing and computer vision domains. This algorithm performs template matching by comparing shape similarity between two images. It quantifies the shape difference between images by calculating the Hausdorff distance, which measures the maximum distance between corresponding points in two point sets. In implementation, the algorithm typically involves extracting edge features from both template and target images using operators like Canny or Sobel, then computing the bidirectional Hausdorff distance between the feature point sets. Key functions in the implementation include point set normalization, distance transformation for efficiency optimization, and threshold-based matching validation. This algorithm demonstrates robust performance in various applications including object recognition, image retrieval, and image registration tasks. It enables accurate localization of target objects within images and supports efficient search and matching operations in large-scale image databases. The Hausdorff distance approach proves particularly valuable for handling partial occlusions and shape variations, making it an essential tool for enhancing the effectiveness and precision of image processing and computer vision systems.