In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Global Optimization
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Framework for Joint Segmentation and Registration
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Statistics-Based Approach to Binary Image Registration with Uncertainty Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Detection and Classification of Traffic Signs
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Parametric estimation of affine deformations of planar shapes
Pattern Recognition
Moments and Moment Invariants in Pattern Recognition
Moments and Moment Invariants in Pattern Recognition
Affine normalization of symmetric objects
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Nonlinear Shape Registration without Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video orbits of the projective group a simple approach to featureless estimation of parameters
IEEE Transactions on Image Processing
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Binary image registration has been addressed by many authors recently however most of the proposed approaches are restricted to affine transformations. In this paper a novel approach is proposed to estimate the parameters of a general projective transformation (also called homography) that aligns two shapes. Recovering such projective transformations is a fundamental problem in computer vision with various applications. While classical approaches rely on established point correspondences the proposed solution does not need any feature extraction, it works only with the coordinates of the foreground pixels. The two-step method first estimates the perspective distortion independently of the affine part of the transformation which is recovered in the second step. As experiments on synthetic as well on real images show that the proposed method less sensitive to the strength of the deformation than other solutions. The efficiency of the method has also been demonstrated on the traffic sign matching problem.