说明：  传统的图像分割方法可以分为基于阈值、边缘、区域的方法等，而传统的分割方法在复杂图像分割中效果不好。为了提高图像分割的性能，我们提出了一种基于局部自适应窗口的Otsu方法。首先，分析了传统阈值法在复杂图像分割中不能很好地形成的原因，比较了全局阈值和局部阈值对图像分割的影响。其次，提出了根据局部信息自适应地改变局部窗口大小的方法，分析了该方法的特点。最后，证明了所提出的新方法优于其他方法。实验结果表明，与其他传统方法相比，该方法能保持更多的细节，获得更满意的结果。
(The traditional image segmentation methods can be divided into thresholding, edge and region methods. The traditional segmentation methods do not perform well in the segmentation of complicated images. In order to improve the performance of image segmentation, we propose an adaptive windowed range-constrained Otsu method based on local information.First, the reason why traditional thresholding methods do not perform well in the segmentation of complicated images is analyzed. Therein, the influences of global and local thresholdings on the image segmentation are compared. Secondly, a method of adaptively changing the local window size based on local information is proposed, and the characteristics of the proposed method are analyzed. Finally, the superiority of the proposed method over other methods, It is validated by the experiments that the proposed method can keep more details and acquire much more satisfying results as compared with the other conventional methods.)