{'fraction_non_alphanumeric': 0.012413793103448275, 'fraction_numerical': 0.001379310344827586, 'mean_word_length': 4.854838709677419, '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}}
Space variant filtering of optic flow for robust three dimensional motion estimation

We test a biologically motivated filtering method [9] for noise decreasing in optical flow fields. We use the task of heading detection from optic flow as a way to estimate improvements of flow fields generated by a standard algorithm. The image sequences which we use for the testing are directly calculated from three dimensional real world data assuming a given self motion. Thus we retain the control about the exact heading and rotation and have ground truth. Not surprisingly, due to the noise and the aperture problem the results for the raw flows are often incorrect. In contrast the filtered flows allow correct heading detection.