{'fraction_non_alphanumeric': 0.016541353383458645, 'fraction_numerical': 0.0, 'mean_word_length': 5.224299065420561, '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}}
Performance analysis of gradient based image edge detection

Edge detection is a dynamic area of research and it gives support to higher level image analysis. Edge detection is a method of image processing used to make points in a digital image with discontinuities, sharp modifiers in the image brightness.  Edge detection methods used in this work are Sobel, Prewitt, Roberts, Laplacian of Gaussian and Canny.  The performance evaluation measures such as accuracy and specificity calculated from the Berkeley Segmentation dataset.  Edge detection is used for object detection which offers various applications like medical field, industrial field, biometrics etc.