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Description
Agra marble inlay work (also known as Parchin Kari, Pacchikari or Pietra Dura) is a traditional craftsmanship wherein carefully shaped hard stones are inserted into an engraved marble matrix, with the Taj Mahal being the best-known example. In spite of its historical importance and popular application to modern items, inlayed objects present significant challenges for material classification due to their complexity and heterogeneous nature.In this paper, we propose an ultrasonic system driven by machine learning algorithms for automated material classification in Agra-type marble inlay work. The acquired immersion ultrasonics data are subjected to pre-processing steps such as alignment, removal of noise artifacts, and feature extraction involving temporal, spectral and energy-related parameters. Material classification is performed based on ranking of features and training of the corresponding classifier.Our findings illustrate the potential of using non-destructive techniques together with a data-centric analysis workflow in order to classify the different materials of interest in complex inlays. In addition to the presented case study, the developed method can be extended to other cultural heritage objects as well.