Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Feature Based Methods for Structure and Motion Estimation
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
Video orbits of the projective group a simple approach to featureless estimation of parameters
IEEE Transactions on Image Processing
Efficient, robust, and fast global motion estimation for video coding
IEEE Transactions on Image Processing
A fast parametric motion estimation algorithm with illumination and lens distortion correction
IEEE Transactions on Image Processing
Multimodality Image Registration by Particle Swarm Optimization of Mutual Information
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Fast and accurate global motion compensation
Pattern Recognition
Variable homography compensation of parallax along mosaic seams
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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The approaches to global motion estimation have been naturally classified into one of two main classes: feature-based methods and direct (or featureless) methods. Feature-based methods compute a set of point correspondences between the images and, from these, estimate the parameters describing the global motion. Although the simplicity of the second step has made this approach rather appealing, the correspondence step is a quagmire and usually requires human supervision. In opposition, featureless methods attempt to estimate the global motion parameters directly from the image intensities, using complex nonlinear optimization algorithms. In this paper, we propose an iterative scheme that combines the feature-based simplicity with the featureless robustness. Our experiments illustrate the behavior of the proposed scheme and demonstrate its effectiveness by automatically building image mosaics.