Specimen Outlining: A Computational Archival Science Approach.

Computational archival science (CAS) provides new pathways for research. Biologists, for example, can perform scientific studies by applying AI/ML to digital biological specimen collections and explore questions that were not possible in the analog world. One such approach is the application of computational methods for specimen outlining to assist with specimen identification, morphometry, and other scientific questions. The challenge is to determine how to computationally generate and represent a specimen’s outline. The research presented in this paper addresses this challenge, through the deployment of elliptical Fourier descriptors (EFDs). The paper describes the image processing pipeline for extracting fish outlines, a key morphological feature, and representing the outlines using EFDs. In addition, our research presents the application of machine learning classification on the EFDs. The resulting dataset is well suited for a variety of machine learning-based downstream analyses, including classification by genus and species. Overall, the classification tests produced a 96.3% accuracy, demonstrating the distinguishing nature of the EFDs, and by proxy, the fish outlines as a whole. Broadly, these results indicate the effectiveness of archival specimen usage in machine learning applications, and demonstrate specimen outlining via Fourier descriptors as a computational archival science approach.