On-line construction of the convex hull of a simple polyline
Information Processing Letters
Application of Affine-Invariant Fourier Descriptors to Recognition of 3-D Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Oriented projective geometry
Appendix—projective geometry for machine vision
Geometric invariance in computer vision
Recognition of planar shapes from perspective images using contour-based invariants
CVGIP: Image Understanding
Semi-local projective invariants for the recognition of smooth plane curves
International Journal of Computer Vision
Extracting group transformations from image moments
Computer Vision and Image Understanding
Visual motion of curves and surfaces
Visual motion of curves and surfaces
Projective Alignment with Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Noise-Resistant Invariants of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Canonical Frames for Planar Object Recognition
ECCV '92 Proceedings of the Second European Conference on Computer Vision
The Geometry and Matching of Curves in Multiple Views
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Camera Pose Estimation and Reconstruction from Image Profiles under Circular Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
3L Fitting of Higher Degree Implicit Polynomials
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
GeoBot: A High Level Visual Perception Architecture for Autonomous Robots
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
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We propose a homography estimation method from the contours of planar regions. Standard projective invariants such as cross ratios or canonical frames based on hot points obtained from local differential properties are extremely unstable in real images suffering from pixelization, thresholding artifacts, and other noise sources. We explore alternative constructions based on global convexity properties of the contour such as discrete tangents and concavities. We show that a projective frame can be robustly extracted from arbitrary shapes with at least one appreciable concavity. Algorithmic complexity and stability are theoretically discussed and experimentally evaluated in a number of real applications including projective shape matching, alignment and pose estimation. We conclude that the procedure is computationally efficient and notably robust given the ill-conditioned nature of the problem.