Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Cross-Weighted Moments and Affine Invariants for Image Registration and Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary Image Registration Using Covariant Gaussian Densities
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Registration of joint geometric and radiometric image deformations
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Parametric estimation of affine deformations of planar shapes
Pattern Recognition
Parametric Estimation of Affine Transformations: An Exact Linear Solution
Journal of Mathematical Imaging and Vision
Affine alignment of compound objects: a direct approach
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this paper we propose a novel method for affine registration of images and point patterns. The method is non-iterative and it directly utilizes the intensity distribution of the images or the spatial distribution of points in the patterns. The method can be used to align images of isolated objects or sets of 2D and 3D points. For Euclidean and similarity transformations the additional contraints can be easily embedded in the algorithm. The main advantage of the proposed method is its efficiency since the computational complexity is only linearly proportional to the number of pixels in the images (or to the number of points in the sets).In the experiments we have compared our method with some other non-feature-based registration methods and investigated its robustness. The experiments show that the proposed method is relatively robust so that it can be applied in practical circumstances.