  A supervised machine learning algorithm, called locally adaptive discriminant
analysis (LADA), has been developed to locate boundaries between identifiable
image features that have varying intensities. LADA is an adaptation of image
segmentation, which includes techniques that find the positions of image
features (classes) using statistical intensity distributions for each class in
the image. In order to place a pixel in the proper class, LADA considers the
intensity at that pixel and the distribution of intensities in local (nearby)
pixels. This paper presents the use of LADA to provide, with statistical
uncertainties, the positions and shapes of features within ultrafast images of
shock waves. We demonstrate the ability to locate image features including
crystals, density changes associated with shock waves, and material jetting
caused by shock waves. This algorithm can analyze images that exhibit a wide
range of physical phenomena because it does not rely on comparison to a model.
LADA enables analysis of images from shock physics with statistical rigor
independent of underlying models or simulations
