一种基于统计平均的图像处理自适应阈值模型

Adaptive Threshold Model Research for Image Processing Based on Statistic and Average Method

用统计力学的思想和方法分析了图像象素灰度值和宏观图像信息之间的关系,用统计回归方法对实验数据进行模拟和曲线拟合,建立了基于Boltzmann统计法的平均灰度值和图像阈值的关系模型。在眼睛盯视输入人机交互系统中,对所建模型编写了程序处理函数和算法,进行了实验分析。结果表明,此方法在图像信息稳定的情况下可以得到更好的效果,用建立的自适应阈值模型可以自动获取阈值。实验中图像平均灰度值在110～230情况下,提取特征信息的准确率达到了95%。

A method that is based on Statistical mechanics has been proposed for analyzing the relationship between pixel gray values and the micro information in an image. After simulating and curve fitting of the experiment data, the image-processing model between average gray value and thresholds was constructed using Boltzmann statistics. In order to verify the effectiveness of the model, some experiments were carried out in an eye-gaze input human computer interaction (HCI) system. The experiment results show that this method can achieve better results as long as the main feature of objects in the image remains the same. In these experiments, thresholds could be automatically gotten in terms of the adaptive threshold model, and when the average gray values of images between 110 and 230, the feature extracting accuracy is 95%.