{'fraction_non_alphanumeric': 0.016506189821182942, 'fraction_numerical': 0.001375515818431912, 'mean_word_length': 4.91869918699187, '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}}
YCoCg color Image Edge detection

Edges are prominent features in images and their analysis and detection are an essential goal in computer vision and image processing. Indeed, identifying and localizing edges are a low level task in a variety of applications such as 3-D reconstruction, shape recognition, image compression, enhancement, and restoration. This paper presents a comparative study on different approaches to edge detection of colour images. The approaches are based on transforming the RGB image to YUV and YCoCg and back to RGB colour model. Edges are detected by Laplacian operator and Gradient Operator. The results show that the colour models can be adopted based on the industry that colours are to be used.