{'fraction_non_alphanumeric': 0.018134715025906734, 'fraction_numerical': 0.0012953367875647669, 'mean_word_length': 5.086614173228346, '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}}
Motion segmentation in RGB image sequence based on hidden MRF and 6D Gaussian distribution

A problem of motion segmentation in RGB image sequence is addressed. An algorithm proposed is based on local motion modeling and pixel labeling approach. An information vector used for labeling consists of six components; three color components and three differences of colors. To develop the labeling algorithm a statistical model of motion sequence, which uses a six-variate Gaussian distribution, is chosen. Moreover, the use of a hidden Markov random field (MRF) framework is proposed in order to carry out the segmentation more accurately. The experimental results of the application of the method to an RGB sequence showing a woman's turning head are included and discussed.