A boosted decision tree approach to shadow detection in scanning electron microscope (SEM) images for machine vision applications.

Scanning electron microscopy is important across a wide range of machine vision applications, and the ability to detect shadows in images could provide an important tool for evaluating attributes of the surfaces being imaged, such as the presence of defects or particulate impurities. One example where the presence of shadows can be important is in the reconstruction of elevation maps from stereo-pair scanning electron microscopy (SEM) images. Shadows can both interfere with determination of matching points for stereoscopic calculations, and confuse shape-from-shading algorithms which rely on pixel intensity gradients to calculate surface slope, leading to inaccurate reconstructions. This paper describes a machine learning method for identifying locations in SEM images impacted by shadows, based on a training set of photographic images. The method could be useful as a means of identifying parts of images likely to suffer from reconstruction artifacts in shape-from-shading-based reconstructions, or as a tool for automated defect identification. The method uses a boosted decision tree machine learning approach to identify shadows based on the features of images. The method is illustrated with four different natural surfaces exhibiting a range of different types of shadow features, and an example is used to illustrate how the method can identify regions likely to be impacted by shadows in reconstructions.