Emmanuelle Frenoux
Papers
2
Total Citations
15
H-Index
2
About
Emmanuelle Frenoux is a researcher whose work lies at the intersection of computer vision and mobile robotics, with a particular focus on visual place recognition. Her key contributions center on developing novel, temporally-aware algorithms that enable robots to understand and categorize their environments more effectively. In her highly cited 2011 paper, "TEMPORAL BAG-OF-WORDS," Frenoux introduced a generative model that leverages temporal integration—using a sequence of frames rather than a single image—to significantly improve recognition accuracy. This foundational work has garnered 12 citations, establishing it as a key reference in the field. She further advanced this area with her innovative application of Bayesian filtering combined with Markov chains and Learned Vector Quantization, demonstrating how probabilistic methods can be used to integrate visual information over time during robot exploration. Frenoux’s research is notable for its elegant, practical approach to solving the challenge of place categorization, directly addressing the real-world problem of how a mobile robot can robustly recognize where it is by learning from its visual history. Her work provides essential building blocks for autonomous navigation and spatial understanding.
Research Focus
Key Achievements
Top Papers
- 1
- 2Visual place recognition using Bayesian Filtering with Markov Chains ∗3 citations · 2011