基于二维相关性的图像分割及快速递推算法

基于二维直方图提出了二维相关性的阈值分割算法。首先,通过邻域平均得到原始图像的平滑图像,由原始图像和平滑图像构造二维直方图,然后根据相关性最大准则选择最佳的二维阈值向量。由于该方法同时考虑了图像像素的灰度信息及其空间邻域信息,与一维阈值相比能得到更好的分割效果。同时为降低二维阈值算法的复杂性,提出了快速递推算法。该算法将二维相关性的计算写成递推形式,减少了大量的重复计算,使得算法的复杂性从O(L4)降低到O(L2),计算时间大为减少,有利于该算法的实时应用。

Two-dimensional Correlation Based Image Segmentation and Its Fast Recursive Algorithm

A two-dimensional correlation image thresholding algorithm is proposed on the basis of the two-dimensional histogram.First,a smoothed image is obtained using the neighbor smoothing technique.The two-dimensional histogram is constructed using the gray value and average gray value of a pixel.The two-dimensional threshold is obtained according to the maximum correlation criterion.Compared to the one-dimensional case,the two-dimensional correlation thresholding method can get better segmentation results,because it considers not only image-pixel gray information but also spatial neighbor information.To reduce computation complexity,a fast recursive algorithm is presented.In the fast recursive algorithm,the computation of two-dimensional correlation is written in the recursive form.Many repeated calculations are avoided.The computation complexity is reduced from O(L4) to O(L2).The computation time is also reduced dramatically.This facilitates the recursive algorithm application in the real-time image processing system.