In this paper, gray-scale of various types of cloud images collected over the Kunming Area were analyzed based on statistical theory and methods in order to achieve recognition of the pattern of the weather conditions. The results show that there are remarkable differences in normal distribution on the gray-scale histogram and the recurrence plot for different weather conditions. It is shown that the gray-scale method is simple, feasible, timely, reliable, and accurate. That would provide theoretical support and methods for meteorological and other related departments.

The Statistical Pattern Recognition of the Weather Conditions Based on the Gray-Scale of Image

The Taylor expansion has shown in many fields to be an extremely powerful tool. In this paper, the authors investigated image features and their relationships by analogy with Taylor expansion. The kind of expansion could be helpful for analyzing image feature and engraftment, such as transferring color between images. By analogy with Taylor expansion, the image color transfer algorithm is designed by the first and second-order information. The luminance histogram represents the first-order information of image, and the co-occurrence matrix represents the second-order information of image. Some results illustrate the proposed algorithm is effective. In this study, each polynomial in the Taylor analogy expansion of images is considered as one of image features which help in re-understanding images and its features. By using the proposed technique, the features of image, such as color, texture, dimension, time series, would be not isolated but mutual relational based on image expansion.

Image Color Transfer Approach by Analogy with Taylor Expansion

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Image Color Transfer Approach by Analogy with Taylor Expansion