{'fraction_non_alphanumeric': 0.010679611650485437, 'fraction_numerical': 0.0, 'mean_word_length': 5.064705882352941, '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}}
Mathematical Morphology and Its Application in Machine Vision

Mathematical morphology provides an efficient tool for image analysis. We study the problem of flaw detection in materials which are represented by very poor contrast digital images. An algorithm for flaw detection in the case of glass matte surfaces has been developed. The object skeletons within the binary images are obtained and directional connectivity information in the skeletons is used to discriminate noise patterns from flaws according to a specified criteria. After the discrimination process, the remaining skeletons correspond to flaws and can be employed to recover the shape of flaws. An alarm flag may be turned on if the sizes of the detected flaws are found to exceed industrial standards. In the case of a grayscale image, the image is converted to a binary version by using an adaptive threshold algorithm, then the algorithm for binary images is applied. Experimental results have been obtained for both binary and grayscale digital image data.