Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Philip Heller
Second Advisor
Nada Attar
Third Advisor
Maya deVries
Keywords
Mask R-CNN, computer vision, autonomous reef monitoring structures, machine learning, histogram equalization, segmentation
Abstract
Sponges play a vital role in marine ecosystems, being the only organisms capable of converting dissolved organic matter (DOM) into particulate organic matter (POM). They provide nutrients for coral reefs to thrive in oligotrophic waters. Autonomous reef monitoring structures (ARMS) are used to measure the biodiversity of coral reefs by simulating the complex cavities inside reef structures. Organisms settle on them and scientists can retrieve them after a period of time for analysis. Images are taken of ARMS plates after they are retrieved. Human analysis is unsuitable for the analysis of ARMS plates due to the huge number of images. Computer vision is leveraged to automate the process using machine learning models such as Mask R-CNN. A possible improvement is to preprocess the images using histogram equalization before training the model.
Recommended Citation
Ng, Barry, "Identifying Red Sponges on ARMS Plates by Preprocessing Images using Histogram Equalization" (2025). Master's Projects. 1516.
DOI: https://doi.org/10.31979/etd.f93g-3ssx
https://scholarworks.sjsu.edu/etd_projects/1516
