灰度图像最小误差阈值分割法的二维推广

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Two-dimensional Extension of Minimum Error Threshold Segmentation Method for Gray-level Images

1.
Department of Information and Control, Xi'an Institute of Post and Telecommunications, Xi'an 710061;
2.
School of Electronic Engineering, Xidian University, Xi'an 710071

摘要: 一维最小误差阈值法假设了目标和背景的灰度分布服从混合正态分布. 考虑到噪声等因素对图像质量的影响, 本文在二维灰度直方图上, 基于二维混合正态分布假设, 给出一维最小误差阈值法的二维推广表达式. 为了提高算法的运行速度, 也给出了快速递推算法. 实验表明, 二维最小误差阈值法是一个有效的图像分割算法, 能够更好地适应目标和背景方差相差较大的图像及噪声图像的分割问题.
- 阈值分割 /
- 最小误差阈值法 /
- 二维灰度直方图
Abstract: One-dimensional minimum error thresholding method assumed that the histogram distributions of object and background are governed by a mixture Gaussian distribution. Considering the affects of noise and other factors on image quality, based on the assumption of a two-dimensional mixture Gaussian distribution, a two-dimensional expression of the minimum error thresholding method on the two-dimensional gray-level histogram is proposed. In order to improve the running speed, the fast recursive formulas are also given. Experimental results show that the two-dimensional minimum error thresholding method is a valuable image segmentation method, and can be well adapted to the images with noises and large variances between object and background.
- Threshold segmentation /
- minimum error thresholding method /
- two-dimensional gray-level histogram