{'fraction_non_alphanumeric': 0.02158273381294964, 'fraction_numerical': 0.0035971223021582736, 'mean_word_length': 5.585798816568047, '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}}
An improved motion estimation algorithm using complex wavelets

This paper describes some improvements to our hierarchical wavelet-based algorithm for estimating motion in digital video (Magarey and Kingsbury, 1996). The algorithm uses a separable discrete wavelet transform (DWT) to analyse each frame into a multiresolution pyramid of subimages. Our analysis filters are Gabor-like (complex valued), so each subband at a given level of the pyramid contains information from a distinct spatial orientation. Starting at the coarsest (most sparsely sampled) level, our algorithm produces a single field of motion estimates from the oriented subimages at that level. Accompanying each motion estimate is a directionally dependent confidence measure. The estimate field is then progressively refined by incorporating the finer level subimages, using a standard coarse-to-fine control strategy. This paper describes an iterative strategy including a new interpolation scheme which increases accuracy at a given level, and a curvature correction method which improves the propagation of estimates from coarse to fine.