{'fraction_non_alphanumeric': 0.015313935681470138, 'fraction_numerical': 0.0, 'mean_word_length': 5.228571428571429, '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}}
Non-rigid point set registration: A bidirectional approach

In this paper we present a novel point set registration algorithm based on the robust Gaussian Mixture Model(GMM). We take advantage of a robust estimation to weigh the noise component in GMM. Moreover, a bidirectional EM process is introduced to model outliers in both point sets in contrast to traditional methods. The performance of the method is demonstrated and validated in carefully designed synthetic data and point sets extracted from medical images. Results show that the proposed method can improve the robustness and accuracy as compared to the traditional registration techniques.