T.P. Mithun Haridas

About

T.P. Mithun Haridas is a researcher at the forefront of deep learning applications for marine and underwater image analysis. His primary research focuses on semantic segmentation of underwater imagery, leveraging advanced convolutional neural network architectures to address the unique challenges posed by aquatic environments. Haridas’s most influential work, "Semantic Segmentation of Underwater Images using UNet architecture based Deep Convolutional Encoder Decoder Model" (2021), has garnered 38 citations, establishing a foundational approach for automated underwater scene understanding. This work demonstrates how UNet-based models can effectively segment complex underwater scenes, enabling applications in robotic vision, augmented reality, and marine resource exploration. In a related study, "Analysis of U-Net Based Image Segmentation Model on Underwater Images of Different Species of Fishes" (2021), Haridas systematically compared segmentation performance across diverse fish species, providing critical insights for marine biology and conservation efforts. His contributions are particularly valuable for advancing autonomous underwater vehicles and environmental monitoring systems. By bridging computer vision and marine science, Haridas’s research continues to shape how deep learning techniques are applied to explore and understand the vast, precious biological resources hidden beneath the ocean’s surface.

Research Focus

Key Achievements

Top Papers

1. 1
   Semantic Segmentation of Underwater Images using UNet architecture based Deep Convolutional Encoder Decoder Model

   38 citations · 2021
2. 2
   Analysis of U-Net Based Image Segmentation Model on Underwater Images of Different Species of Fishes

   6 citations · 2021

Key Collaborators

Contact & Links