Edwilson Silva Vaz

Universidade Federal do Rio Grande, University of Rio Grande and Rio Grande Community College

Papers

Total Citations

H-Index

About

Edwilson Silva Vaz is a researcher whose work sits at the intersection of computer vision and robotics, tackling two of the most challenging visual environments: underwater scenes and industrial welding. His primary research areas include underwater image restoration, water classification for depth estimation, and machine vision for automated welding systems. Vaz has made significant contributions by developing monocular image-based methods that classify water types and estimate depth—critical for improving robotic perception in murky, light-absorbing underwater environments. His 2020 paper on underwater depth estimation using water classification has garnered 3 citations, while his 2021 work on water classification from monocular images adds another 2 citations, reflecting growing interest in this niche. In parallel, Vaz addresses the industrial challenge of welding automation, where his 2018 papers on reducing fume interference in camera lenses and restoring images affected by welding fume (each with 2 citations) propose practical solutions for robot vision in harsh, smoky conditions. His work is notable for bridging the gap between theoretical image processing and real-world robotic applications, making him a key figure in advancing autonomous systems for both deep-sea exploration and manufacturing.