The Chow Form of the Essential Variety in Computer Vision
Gunnar Fl{\o}ystad, Joe Kileel, Giorgio Ottaviani
TL;DR
This paper derives a formula for the Chow form of the essential variety in computer vision using advanced algebraic geometry techniques, enabling detection of noisy point correspondences between images.
Contribution
It introduces a novel algebraic approach to compute the Chow form of the essential variety, combining secant varieties, Ulrich sheaves, and representation theory.
Findings
The formula accurately detects noisy point correspondences.
Numerical experiments validate the effectiveness of the derived formula.
The approach advances algebraic methods in computer vision applications.
Abstract
The Chow form of the essential variety in computer vision is calculated. Our derivation uses secant varieties, Ulrich sheaves and representation theory. Numerical experiments show that our formula can detect noisy point correspondences between two images.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.