Rosemol Thomas

Cochin University of Science and Technology

Papers

Total Citations

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About

Rosemol Thomas has made impactful contributions at the intersection of computer vision and marine biology, with a primary focus on underwater image segmentation. Her most-cited work, "Analysis of U-Net Based Image Segmentation Model on Underwater Images of Different Species of Fishes" (2021), addresses a critical challenge in robotic vision and marine resource exploration. By systematically comparing the performance of U-Net architectures on challenging underwater imagery, Thomas has advanced methods for automating fish species identification and segmentation—a task essential for navigating vast marine biological resources and gene banks. Her research directly supports applications in autonomous underwater vehicles, virtual reality, and augmented reality systems. With 6 citations, this work has already begun influencing subsequent studies in marine computer vision. Thomas’s contributions are particularly valuable for researchers and students working at the nexus of deep learning and environmental monitoring, demonstrating how state-of-the-art segmentation models can be adapted to overcome the unique visual distortions of underwater environments. Her work represents a meaningful step toward scalable, automated exploration of aquatic ecosystems.