基于改进最大类间方差法的手势分割方法研究

针对手势图像中由于噪声和成像干扰造成的手势模糊和边界不清晰的问题，提出了一种基于改进最大类间方差法的手势分割方法.首先建立手势图像的二维灰度直方图，在二维灰度直方图上确定噪声点位置，在原图的相应区域滤除噪声.然后重建二维灰度直方图将内点区的点集投影到45度线，得到投影灰度直方图.接下来在灰度投影直方图上采用全局Otsu确定局部Otsu的左边界，用高斯函数拟合得到局部Otsu右边界，最后采用局部Otsu分割手势.该方法可以有效地对手势图像进行精确分割，实验结果验证了本文算法的有效性.

Gesture Segmentation with Improved Maximum Between-clusterVariance Algorithm

In this paper, in order to solve the problem of ambiguity or unclear boundary caused by noise and interference in gesture imaging, a gesture segmentation method based on the improved maximum between-cluster variance algorithm is proposed. Firstly, a two-dimensional gray histogram of gesture image is generated, and positions of noise points are determined on the two-dimensional gray histogram. After filtering noise in the corresponding region of the gesture image, a two-dimensional gray histogram is reconstructed. The point set of the inner point area are projected to the 45 degrees line to generate the gray projection histogram. Then, the global Otsu is used to determine the left boundary of the local Otsu and Gauss function is used to get the right boundary of the local Otsu in the projection gray histogram. Finally, the local Otsu is used to segment the gesture image. This method can effectively segment the gesture image accurately. Experimental results have verified the effectiveness of the proposed algorithm.