The fundamental matrix: Theory, algorithms, and stability analysis

By Q. Luong, Olivier D. Faugeras. International Journal of Computer Vision, 2004

625 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9643cd37f8b36dfb8a4c8bcd087843dec40d6635

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Henrik Iskov Christensen
2. Danica Kragic
3. David G. Lowe
4. Cordelia Schmid
5. Andrew Zisserman
6. Florent Perronnin
7. Kristen Grauman
8. Ondřej Chum

In Computer Vision and Pattern Recognition

1. Henrik Iskov Christensen
2. Danica Kragic
3. David G. Lowe
4. Cordelia Schmid
5. Andrew Zisserman
6. Li Fei-Fei
7. Luc Van Gool
8. Andrea Vedaldi

Citations in the corpus

• Distinctive Image Features from Scale-Invariant Keypoints (2004, background): “A more general solution would be to solve for the fundamental matrix (Luong and Faugeras, 1996; Hartley and Zisserman, 2000).”
• Survey on Visual Servoing for Manipulation (2002, methodology): “In the case of uncalibrated cameras, the relationship between two images is represented using a fundamental matrix, (Zhang et al. 1995), (Luong & Faugeras 1996).”