[5] developed algorithms for time series analysis of satel-Figure : The workflow for autocharacterization of perovskite semiconductors using computer vision. From left to right: A fluid handling system mixes and deposits each unique semiconductor material onto a plate. The vision system captures images in RGB and hyperspectral channels (wavelengths λ ∈ [380nm, 1020nm]), then segments the pixels of each material deposit (X, Y, λ) from the background. The (i) compositional information, (ii) direct band gap, and (iii) stability of each unique sample are automatically characterized using the optical data segmented by the vision system. lite images. Computer vision is gaining traction specifically in rapid morphological material characterization and is increasingly being used to analyze large sets of microscope images . As such, multiple analytical computer vision tools have been developed to access material morphology, often focused on identifying microstructures of materials. For example, Park et al. created a semi-automated image segmentation and analysis algorithm to extract the morphology of nano-particles from image data. Likewise, utilized computer vision and machine learning to detect dendritic microstructures from a database of solder alloy micrographs. There is also extensive work surrounding materials recognition in non-micrographic images using computer vision . In order to support the growing need for micrographs and other experimental image analysis, advances like those made by Li et al. [9], Wang et al. , and Neshatavar et al. on object segmentation and denoising allow for further use cases of computer vision in scientific research.