Image Processing and Machine Learning in Underwater Multimedia Collections

PURPOSE: This project aims to identify marine life and other objects that exist in aquatic environments. Computing algorithms must be developed to process underwater images and perform tasks such as object recognition. SUBJECTS: This work captured and generated large collections of underwater images and video frames. METHODS AND MATERIALS: Distortion algorithms were developed to generate large collections of synthetic, distorted images based on ideal images of fishes. ANALYSES: Software such as OpenCV, TensorFlow, and other algorithms were used to generate and analyze content contained in multimedia datasets. RESULTS: This project curated diverse multimedia collections and prototyped a machine learning framework. CONCLUSIONS: Future work can leverage the deliverables of this project and continue successful machine learning tasks for other fish