Hugo Vieira Neto
Universidade Tecnológica Federal do Paraná, University of Essex
Papers
11
Total Citations
156
H-Index
7
About
Hugo Vieira Neto is a leading researcher in autonomous robotics and visual perception, whose work has fundamentally advanced how mobile robots explore, inspect, and understand dynamic environments. His core research focuses on novelty detection—enabling robots to differentiate between familiar and unfamiliar stimuli—and he has pioneered several algorithms that allow robots to learn environmental models autonomously. His most influential contribution, "Visual novelty detection with automatic scale selection" (44 citations), established a robust framework for robots to identify anomalies without prior knowledge of the scene. Neto further demonstrated the power of incremental PCA for real-time novelty detection (25 citations), a method that allows continuous learning as robots navigate. His work on "Real-time Automated Visual Inspection using Mobile Robots" (30 citations) directly translated these algorithms into practical inspection systems. Beyond visual perception, Neto has innovated in sonar-based obstacle localization using compressed sensing (7 citations), addressing fundamental challenges in autonomous navigation. His research consistently bridges theoretical novelty detection with real-world robotic applications, making him a key figure in developing inspection robots that can operate safely and intelligently in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1Visual novelty detection with automatic scale selection44 citations · 2007
- 2Real-time Automated Visual Inspection using Mobile Robots30 citations · 2007
- 3Incremental PCA: an alternative approach for novelty detection25 citations · 2005
- 4Automated Exploration and Inspection: Comparing Two Visual Novelty Detectors12 citations · 2005
- 5Visual novelty detection for autonomous inspection robots11 citations · 2006
- 6Novelty-based visual inspection using mobile robots10 citations · 2004
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