Abstract
不同的时间季节有不同的特征，根据季节的特征，我们在计算机上利用各种方法可以清楚的识别不同的季节。本文利用HSV图像色彩模型，主要对各个季节的图片的特征进行分析和研究，进行颜色间的对比，比较其色彩值的平均值与方差，运用光学图像处理技术，采用近邻分类的方法，对比各季节图像的特征差异，对所选图像进行分类与识别，进而达到自动识别季节的目的。实验结果证实了此研究方法的可行性，在季节识别上能够达到自动识别的效果。 Different time seasons have different characteristics. According to the characteristics of the season, we use various methods on the computer to clearly identify different seasons. Based on HSV color model, the characteristics of the images of each season are analyzed and studied, and in the colors contrast, the mean value and variance of them are compared. Through using the optical image processing technology, adopting the nearest neighbor classification method, comparing the characteristics of different season images, and then classifying and recognizing the selected images, thus the season can be identified automatically. The experimental results confirm the feasibility of this method, and can achieve high performance in season recognition.
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