基于二维最大相关准则的图像阈值分割

IMAGE THRESHOLDING SEGMENTATION BASED ON TWO-DIMENSIONAL MCC

提出了基于二维最大相关准则的自动阈值图像分割算法.该方法根据图像的二维直方图中目标和背景分布的相关量最大来选择阈值,能够实现比传统最大相关准则更强的抗噪声能力.同时将遗传算法用于对二维最大相关准则阈值分割的优化,试验结果表明该算法可以实现快速、准确图像分割.

Automatic image thresholding segmentation based on two-dimensional Maximum Correlation Criterion （MCC） was proposed. On the base of two-dimensional histogram, this method maximizes the correlations associated with the distributions of the background and object classes to obtain the optimal threshold, and it has greater resistance capability to noise than one-dimensional MCC. At the same time, Genetic Algorithms （ GA ） was used in the establishment of the optimal threshold based on the two-dimensional MCC. The experimental results show that the proposed algorithm achieves a good segmentation quality and shorten the computational time.