Workpiece Image Preprocessing with Enhanced Genetic Algorithm and Hausdorff Distance

This program performs workpiece image preprocessing and edge extraction operations, combining enhanced genetic algorithms with Hausdorff distance to achieve object recognition. The implementation uses Canny edge detection as matching features (involving Gaussian filtering, gradient calculation, non-maximum suppression, and double thresholding), employs modified Hausdorff distance as similarity measurement criteria (with statistical outlier rejection for robustness), and utilizes genetic algorithms for rapid optimal matching search (featuring chromosome encoding of transformation parameters, fitness evaluation, and selection/crossover/mutation operations). This successfully achieves target object matching and recognition. Additionally, the program can be applied to image processing and recognition tasks in other domains, offering expanded possibilities and convenience for various application scenarios through modular code architecture that allows customization of feature extractors and matching algorithms.