{'fraction_non_alphanumeric': 0.017276422764227643, 'fraction_numerical': 0.0, 'mean_word_length': 5.118012422360248, '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}}
A novel approach to the suppression of false contours originated from Laplacian-of-Gaussian zero-crossings

A novel criterion is introduced for eliminating false contours detected as zero-crossings in images processed by means of a Laplacian-of-Gaussian filter. Each candidate contour point is given a score and is retained only if such a value exceeds a threshold which is related to image contrast and independent of the noise. After all the zero-crossings of a contour have been classified, the whole contour is either accepted or rejected depending on its percentage of validated contour points. The threshold percentage is stated a function of the signal-to-noise ratio. The algorithm effectively copes with images taken practically under any possible environmental conditions during acquisition. Experiments carried out on images of structured markers show that the procedure is robust to noise and suitable for real-time applications in which an image segmentation is demanded.