{'fraction_non_alphanumeric': 0.0187207488299532, 'fraction_numerical': 0.0, 'mean_word_length': 5.618556701030927, '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}}
Analysis of moment performance

In this paper, we analyze and compare the performance, both in spatial and frequency domains, of different moments. Our analysis shows that Legendre and Gaussian-Hermite moments better separate image features based on different spatial modes and Gaussian-Hermite moments are less sensitive to noise. Analysis on spectral performance shows that Legendre and Gaussian-Hermite moments separate different frequency bands more effectively than others. It is also shown that Gaussian-Hermite moments give an approach to construct orthogonal features from wavelet analysis results. Experimental results are reported.