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  1. ; ; ; ; (, 2021 IEEE MIT Undergraduate Research Technology Conference (URTC))
    This work explores the use of a triplet neural net-work for assessing the similarity of paper textures in a collection of Henri Matisse’s lithographs. The available dataset contains digital photomicrographs of papers in the lithograph collection, consisting of four views: two raking light orientations and both sides of the paper. A triplet neural network is first trained to extract features sensitive to anisotropy, and subsequently used to ensure that all papers in the dataset are in the same orientation and side. Another triplet neural network is then used to extract the texture features that are used to assess paper texture similarity. These results can then be used by art conservators and historians to answer questions of art historical significance, such as artist intent. 
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