Gustavo Carneiro
University of Adelaide, Australian Centre for Robotic Vision
Papers
11
Total Citations
303
H-Index
8
About
Gustavo Carneiro is a leading researcher at the intersection of computer vision, deep learning, and robotics, with a particular focus on medical image analysis and autonomous surgical systems. His work centers on bringing semantic understanding to robotic perception, enabling machines to interpret complex visual environments with greater accuracy and reliability. Carneiro has made significant contributions to the development of probabilistic object detection, introducing the Probability-based Detection Quality (PDQ) metric to evaluate detection uncertainty—a critical advancement for safety-critical applications like autonomous surgery. His highly cited survey on semantics for robotic mapping, perception, and interaction (over 140 combined citations) has become a foundational reference in the field. In medical imaging, Carneiro has pioneered deep learning methods for automatic segmentation of femoral cartilage in ultrasound images, directly supporting robotic knee arthroscopy guidance. His Bayesian CNN approach for segmentation uncertainty inference on 4D ultrasound data (20 citations) addresses the challenge of inhomogeneous tissue appearance, improving surgical precision. With multiple papers from 2020 alone accumulating over 280 citations, Carneiro’s work is shaping the future of autonomous robotic systems that can perceive, understand, and act safely in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Semantics for Robotic Mapping, Perception and Interaction: A Survey100 citations · 2020
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- 3Semantics for Robotic Mapping, Perception and Interaction: A Survey44 citations · 2020
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- 6Probabilistic Object Detection: Definition and Evaluation16 citations · 2020
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- 9A probabilistic challenge for object detection4 citations · 2019
- 10Special Issue on Deep Learning for Robotic Vision3 citations · 2020