{'fraction_non_alphanumeric': 0.03178206583427923, 'fraction_numerical': 0.00340522133938706, 'mean_word_length': 5.1678321678321675, '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}}
Contour extraction by multi-level active contour model

In this paper, an innovative algorithm for object segmentation and contour extraction is proposed, where the active contour evolution based on the Mumford-Shah model is performed on a coarse-to-fine approach spanned by wavelet transform. The multi-level active contour model consists of three main parts: 1). wavelet decomposition for obtaining multi-scale image; 2) in the top-level, image wavelet-based edge detection to get an initial evolving contour (initialization procedure); 3) evolving contour based on the Mumford-Shah model in each level, from top level to down level. The experiments and analysis demonstrate that the whole calculation on multi-objects contour extraction can be greatly decreased by the benefit of coarse-to-fine strategy and ideal noise resistance ability can also be expected in this algorithm.