V. Masilamani

About

V. Masilamani is a researcher at the forefront of information security and image processing, with a particular focus on applications for robotics and telemedicine. His work centers on developing robust methods for protecting digital data, including reversible data hiding (RDH) and zero-watermarking techniques. A key contribution is his 2018 paper on "Reversible Data Hiding Scheme During Encryption Using Machine Learning," which has garnered 45 citations for its novel approach to securing medical images in telemedicine applications. Masilamani has also advanced zero-watermarking for underwater images captured by robots, protecting copyrights without altering the original image—a critical feature for mission-critical surveillance. Additionally, he has addressed image quality assessment for robotic systems, developing a no-reference sharpness measure using DCT statistics and multivariate adaptive regression splines. This work is essential for ensuring reliable visual data in autonomous systems. Through these contributions, Masilamani bridges the gap between information security and practical robotic applications, making digital data transmission both safer and more reliable.

Research Focus

Key Achievements

Top Papers

1. 1
   Reversible Data Hiding Scheme During Encryption Using Machine Learning

   45 citations · 2018
2. 2
   Zero-Watermarking in Transform Domain and Quadtree Decomposition for Under Water Images Captured by Robot

   7 citations · 2018
3. 3
   No-reference Image Sharpness Measure using Discrete Cosine Transform Statistics and Multivariate Adaptive Regression Splines for Robotic Applications

   6 citations · 2018

Key Collaborators

Contact & Links