国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Parameter optimization of automatic image processing program is a time-consuming process. For complex image analysis tasks with high noise and shadow, manual adjustment of parameters can not produce good results. In order to optimize multiple parameters simultaneously, a parameter adaptive model based on feedback is proposed to improve the standard image segmentation method. The performance of the algorithm is compared by adjusting its parameters. The algorithm is evaluated and compared according to the benchmark data set to discuss the influence of image shadow and noise on segmentation and classification accuracy. The results show that, in different shadow levels, the image segmentation and classification effect of adaptive feedback parameter is better than that of feedforward algorithm. When there are only abstract reference data, this method is effective in performing automatic image analysis. At the same time, hierarchical data sets are used to evaluate the robustness of different image processing, which is beneficial to the end user for image processing. It provides reference for the theoretical research and practice of automatic image processing.