基于Tsallis广义散度的图像阈值分割

Image Threshold Segmentation Based on Tsallis Generalized Divergence

为了有效获取图像自动分割的最佳阈值,基于Tsallis广义散度概念,提出了一种新的图像阈值化方法。首先,对Tsallis广义散度公式进行化简,进而建立该简化公式的对称形式。接着,在简化公式的对称形式上构造阈值化前后图像前景与背景的散度和,然后对该和式求取极小值获取图像分割的最佳阈值。实验结果表明,新方法是可行的且能更好的适应复杂多样的图像,是一个有效的阈值分割方法。

In order to obtain the optimal threshold for automatic image segmentation efficiently, a novel image thresholding method is proposed based on the conception of Tsallis generalized divergence. First, the original formula of Tsallis generalized divergence was simplified, and then the symmetrical version was constructed. Subsequently, the divergence sum of the foreground and background between original and thresholded image was set up based on the symmetric equation. Finally, the sum formula was minimized to obtain the optimal threshold. The experimental results show that the proposed method is feasible and has better adaptability on complicated images. So, it is an available threshold selection method.