论文标题
OTSU方法的概括和最小误差阈值
A Generalization of Otsu's Method and Minimum Error Thresholding
论文作者
论文摘要
我们提出了广义直方图阈值（GHT），这是一种基于直方图的图像阈值的简单，快速且有效的技术。 GHT通过对高斯人与适当先验的混合物的后验估计进行大约最大的后验估计来起作用。我们证明GHT将三种经典的阈值技术归为特殊情况：OTSU方法，最小误差阈值（MET）和加权百分位数阈值。 GHT因此可以在这三种算法之间进行连续的插值，从而可以显着提高阈值精度。 GHT还提供了对在阈值期间使直方图的垃圾箱宽度的共同实践的澄清解释。我们表明，GHT在最近的手写文档图像二线化（包括经过培训的人均二聚体训练的深神经网络）上胜过所有算法的性能或匹配所有算法的性能，并且可以在十二行代码中实现，或者作为对Ottsu的方法或MET的三重修改。
We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu's method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram's bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu's method or MET.
