Application of Affine-Invariant Fourier Descriptors to Recognition of 3-D Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Dyadic Wavelet Affine Invariant Function for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Reconstruction of General Curves, Using Factorization and Bundle Adjustment
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A SIFT Descriptor with Global Context
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Wavelet Approximation-Based Affine Invariant Shape Representation Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projective Reconstruction from Multiple Views with Minimization of 2D Reprojection Error
International Journal of Computer Vision
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projective reconstruction of ellipses from multiple images
Pattern Recognition
Parametric estimation of affine deformations of planar shapes
Pattern Recognition
Learning a discriminative classifier using shape context distances
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Undoing the affine transformation using blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents a subspace approach to matching a pair of 2D shapes, and estimating the affine transformation that aligns the two 2D shapes. In the proposed method, by considering each shape as a 2D signal, one shape is projected onto the subspace spanned by the other, and the affine transformation is estimated by minimizing the projection error in the subspace. The proposed method is fast, easy to implement, and with a clear physical interpretation. Furthermore, it is robust to noise due to the merit of the subspace method. The proposed approach has been tested for registration accuracy, computation time, and robustness to noise. Its performance on synthetic and real images is compared with the state-of-the-art reference algorithms. The experimental results show that our approach compares favorably to the reference methods, in terms of registration accuracy, computation speed, and robustness.