基于模糊熵的GLLE熵阈值分割方法

图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法，被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用，引入一种GLLE（Gray Level and Local Entropy）二维直方图改进熵阈值图像分割模型，并提出了基于模糊熵的方法计算所建立的二维直方图模型.通过标准实验数据集上的对比实验表明，基于模糊熵的GLLE熵阈值分割方法可以得到更加准确的阈值，提高了分割精度.同时在处理不同类型图像的表现上优于往常的算法，具有更强的鲁棒性.

GLLE entropic threshold segmentation based on fuzzy entropy

Image segmentation is a basic and important issue in field of computer vision.Entropy threshold image segmentation,as an effective segmentation method,is widely used in pattern recognition and image processing.Traditional image segmentation methods cannot obtain enough effective image features.In order to solve this problem and further explore the application of entropy threshold in image segmentation,a GLLE (Gray Level and Local Entropy) two-dimensional histogram is introduced to improve the entropy threshold image segmentation model,and a method based on fuzzy entropy is proposed to calculate the established two-dimensional histogram model.The comparison experiments on standard experimental datasets show that the proposed GLLE entropy threshold segmentation method based on fuzzy entropy can get more accurate thresholds and improve the segmentation accuracy.Compared with traditional algorithms,our method performs better on different types of images,and has stronger robustness.