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Thresholding method for segmenting gray scale image, method for determining background concentration distribution, and image displacement detection method

机译:用于分割灰度图像的阈值方法,确定背景浓度分布的方法和图像位移检测方法

摘要

In a thresholding method for segmenting gray scale image which is less dependent on the concentration distribution of the input image, the threshold in determined as a zero-cross point of a histogram difference between the concentration histograms for the gray scale image within the window at a window position at which the window contains more high concentration region and at a window position at which the window contains more low concentration region. Also, in a method of determining a background concentration distribution from an image having an arbitrarily distributed pattern, the background concentration level is determined as a peak concentration or a mean value concentration of a low concentration section of the histogram difference of the concentration histograms for two associated windows. Also, in an image displacement detection method capable of detecting a displacement of a pattern in a two dimensional image accurately by using only a one-dimensional pattern matching without any period mismatching error even for an image of a repetitious pattern, the discplacements in the vertical and lateral direction which are locally minimum and for which a discrepancy in an oblique direction is minimum are selected as the actual displacements.
机译:在用于分割不太依赖于输入图像的浓度分布的灰度图像的阈值化方法中,将阈值确定为窗口处的灰度图像的浓度直方图之间的直方图差的直方图差的零交叉点。窗口包含更多高浓度区域的窗口位置以及窗口包含更多低浓度区域的窗口位置。而且,在从具有任意分布的图案的图像确定背景浓度分布的方法中,将背景浓度水平确定为两个浓度直方图的直方图差的低浓度区间的峰值浓度或平均值浓度。关联的窗口。而且,在即使对于重复图案的图像也能够通过仅使用一维图案匹配而没有任何周期失配误差而能够准确地检测二维图像中的图案的位移的图像位移检测方法中,垂直方向上的错位选择局部最小且横向方向上的偏差最小的横向和横向方向作为实际位移。

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