Abstract:
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.
