{'fraction_non_alphanumeric': 0.021809369951534735, 'fraction_numerical': 0.0016155088852988692, 'mean_word_length': 4.9855072463768115, '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}}
Application of categorical colors to area segmentation for road image

Humans can put different colors together and categorize them as "red", "yellow", or "orange" etc. This is called categorical color perception. We applied this property of human color vision to area segmentation for road images in order to compensate color tone change of road images depending on light condition on a road. Basic map of categorical colors is constructed in the L*a*b* space based on the color naming experiment. Area segmentation was done by assigning one of the 14 categorical colors to each pixel. Results were successful, even without any noise reduction technique, A shifted database of categorical colors for images with orangish tone is also prepared by trial and error. Pseudo-color-constancy is successfully obtained for the images of orangish tones using the shifted database. To deal with the lightness change depending on the change of sunlight along the time of day, an appropriate value was added to the lightness of each pixel of the original image. Satisfiable area segmentation was obtained in this case, too. This method indicates the possibility of implementation of color constancy property for color image processing of road scene.