Papers
11
Total Citations
130
H-Index
7
About
Nicola Greggio is a researcher whose work bridges computer vision, robotics simulation, and machine learning. His primary research areas include image segmentation, Gaussian mixture models (GMMs), and humanoid robot simulation, with a particular focus on enabling robots to perceive and interact with their environments. Greggio’s most significant contribution is his work on fast estimation of Gaussian mixture models for image segmentation, which has garnered 41 citations and provides an efficient method for robots to parse visual data in real time. He also developed a robust algorithm for least-square fitting of ellipses, applied to the RobotCub humanoid platform, and advanced 3D stereo tracking of spherical objects using the iCub robot. In simulation, Greggio created realistic models of humanoid soccer robots for RoboCup and USARSim environments, contributing to the development of autonomous behaviors in competitive and search-and-rescue scenarios. His work on greedy estimation of mixture models via binary tree search further showcases his innovative approach to unsupervised learning. With over 130 total citations across his top papers, Greggio’s research has had a tangible impact on both theoretical and applied robotics, particularly in enabling efficient, real-time perception and simulation for humanoid platforms.
Research Focus
Key Achievements
Top Papers
- 1Fast estimation of Gaussian mixture models for image segmentation41 citations · 2011
- 2Simulation of small humanoid robots for soccer domain19 citations · 2009
- 3RobotCub implementation of real-time least-square fitting of ellipses13 citations · 2008
- 4
- 5Efficient greedy estimation of mixture models through a binary tree search10 citations · 2014
- 6A realistic simulation of a humanoid robot in USARSim10 citations · 2007
- 7
- 8Image Segmentation for Robots: Fast Self-adapting Gaussian Mixture Model7 citations · 2010
- 9A 3D Model of a Humanoid for USARSim Simulator5 citations · 2006
- 10