Emma Regentova
Papers
1
Total Citations
9
H-Index
1
About
Dr. Emma Regentova is a computer vision researcher whose work focuses on robust feature extraction and geometric inference from natural scenes. Her most-cited study, "An Experimental Evaluation of Different Features and Nodal Costs for Horizon Line Detection" (2014), systematically compares image descriptors and cost functions for horizon estimation—a critical task in autonomous navigation, augmented reality, and scene understanding. By rigorously testing multiple feature types (e.g., edges, textures, color distributions) and nodal cost formulations, she provides a benchmark that clarifies which visual cues are most reliable under varying environmental conditions. This work has garnered 9 citations, serving as a practical guide for researchers developing horizon-detection algorithms. Dr. Regentova’s contributions lie in bridging theoretical feature analysis with real-world performance, offering actionable insights for improving the accuracy and robustness of geometric vision systems. Her research is particularly valuable for students and engineers working on outdoor perception tasks, where horizon detection is a foundational step. Through careful experimental design, she advances the field’s understanding of how to select and combine visual features for reliable spatial reasoning.
Research Focus
Key Achievements
Top Papers
- 1