We present an approach for automatic threshold segmentation of greyscale images. The procedure is inspired by a reinterpretation of the strategy observed in human operators when adjusting thresholds manually and interactively by means of ‘slider’ controls. The approach translates into two methods. The first one is suitable for single or multiple global thresholds to be applied globally to images and consists of searching for a threshold value that generates a phase whose boundary coincides with the largest gradients in the original image. The second method is a variation, implemented to operate on the discrete connected components of the thresholded phase (i.e. the binary regions) independently. Consequently, this becomes an adaptive local threshold procedure, which operates relative to regions, rather than to local image subsets as is the case in most local thresholding methods previously published. Adding constraints for specifying certain classes of expected objects in the images can improve the output of the method over the traditional ‘segmenting first, then classify’ approach.

中文翻译：

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从区域边界的梯度自动阈值化

我们提出了一种灰度图像自动阈​​值分割的方法。该程序的灵感来自对人类操作员通过“滑块”控件手动和交互调整阈值时观察到的策略的重新解释。该方法转化为两种方法。第一个适用于将单个或多个全局阈值全局应用于图像，包括搜索阈值，该阈值生成的相位与原始图像中的最大梯度重合。第二种方法是一种变体，用于独立地对阈值相位的离散连接分量（即二进制区域）进行操作。因此，这变成了一个自适应局部阈值程序，它相对于区域进行操作，而不是像以前发布的大多数局部阈值方法那样对局部图像子集进行处理。添加用于指定图像中某些类别的预期对象的约束可以改善该方法的输出，而不是传统的“先分割，然后分类”方法。