{'fraction_non_alphanumeric': 0.015151515151515152, 'fraction_numerical': 0.0, 'mean_word_length': 5.342465753424658, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Region Adjacency Tree Approach to the Detection and Design of Fiducials

We report a topological approach to fiducial recognition for real-time applications. Independence from geometry makes the system tolerant to severe distortion, and allows encoding of extra information. The method is based on region adjacency trees. After describing the mathematical foundations, we present a set of simulations to evaluate the algorithm and optimise the fiducial design.