论文标题
使用内核密度估计和规模空间表示的多类直方图阈值
Multiclass histogram-based thresholding using kernel density estimation and scale-space representations
论文作者
论文摘要
我们提出了一种基于非参数核密度（KD）估计的直方图多类阈值的新方法，其中使用预期最大化（EM）迭代定义了KD估计的未知参数。该方法比较了KD估计的提取的最小值的数量与所需簇的数量减去一个。如果这些数字匹配，则算法将最小值的位置返回阈值值，否则，该方法逐渐减小/增加内核带宽，直到数字匹配为止。我们使用具有已知阈值的合成直方图验证该方法，并使用真实X射线计算机断层扫描图像的直方图。实际直方图阈值后，我们估算了样品的孔隙率，并将其与直接实验测量值进行了比较。比较显示了阈值的有意义。
We present a new method for multiclass thresholding of a histogram which is based on the nonparametric Kernel Density (KD) estimation, where the unknown parameters of the KD estimate are defined using the Expectation-Maximization (EM) iterations. The method compares the number of extracted minima of the KD estimate with the number of the requested clusters minus one. If these numbers match, the algorithm returns positions of the minima as the threshold values, otherwise, the method gradually decreases/increases the kernel bandwidth until the numbers match. We verify the method using synthetic histograms with known threshold values and using the histogram of real X-ray computed tomography images. After thresholding of the real histogram, we estimated the porosity of the sample and compare it with the direct experimental measurements. The comparison shows the meaningfulness of the thresholding.
