Cristiano Rafael Steffens
Universidade Federal do Rio Grande, University of Rio Grande and Rio Grande Community College
Papers
14
Total Citations
161
H-Index
9
About
Cristiano Rafael Steffens is a computer vision and robotics researcher whose work spans deep learning, image quality enhancement, and industrial automation. His research primarily addresses the intersection of convolutional neural networks and real-world perception challenges, with a particular focus on making autonomous and robotic systems more reliable under imperfect conditions. Steffens has made notable contributions to exposure correction for digital images, demonstrating in his 2018 work (20 citations) that deep learning can effectively recover poorly exposed images beyond the capabilities of then-state-of-the-art methods. His investigations into how exposure, noise, and compression degrade CNN-based recognition (16 citations) have raised important questions about the robustness of autonomous perception systems — a theme further developed in his 2021 work on robustness in robotics. In the industrial domain, Steffens has advanced vision-based robotic welding, developing automated seam tracking and groove measurement systems that reduce human intervention in hazardous environments. He also contributed foundational resources for fire detection research, releasing a challenging non-stationary video dataset (17 citations) that established standardized benchmarks for the field. Collectively, his work has accumulated over 140 citations, reflecting meaningful influence across computer vision, robotics, and industrial automation communities.
Research Focus
Key Achievements
Top Papers
- 1CNN Based Image Restoration25 citations · 2020
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- 3An Unconstrained Dataset for Non-Stationary Video Based Fire Detection17 citations · 2015
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- 10On Robustness of Robotic and Autonomous Systems Perception7 citations · 2021