A Three-Frame Algorithm for Estimating Two-Component Image Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Performance of optical flow techniques
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Parametric Model Fitting: From Inlier Characterization to Outlier Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Image matching with scale adjustment
Computer Vision and Image Understanding
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An iterative region-growing algorithm for motion segmentation and estimation: Research Articles
International Journal of Intelligent Systems - Robotics and Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Over-Parameterized Variational Optical Flow
International Journal of Computer Vision
Global parametric image alignment via high-order approximation
Computer Vision and Image Understanding
Robust motion estimation under varying illumination
Image and Vision Computing
Shadow resistant direct image registration
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Fast motion estimation using bidirectional gradient methods
IEEE Transactions on Image Processing
Color Image Registration under Illumination Changes
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Joint photometric and geometric image registration in the total least square sense
Pattern Recognition Letters
Robust GME in encoded MPEG video
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
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The estimation of parametric global motion had a significant attention during the last two decades, but despite the great efforts invested, there are still open issues. The most important ones are related to the accuracy of the estimation and to the ability to recover large deformation between images. In this paper, a new generalized least squares-based motion estimator is proposed. The non-linear Brightness Constancy Assumption is directly used instead of using the classical approach by linearizing the minimization problem using the optical flow equation. In addition, the proposed formulation of the motion estimation problem provides an additional constraint that helps to match the pixels by using the image gradient in the matching process. That is achieved by means of a weight for each observation, assigning high weight values to the observations considered as inliers, i.e. the ones that support the motion model, and low values to the ones considered as outliers. The accuracy of our approach has been tested using challenging real images using both affine and projective motion models. Two motion estimator techniques that uses iteratively reweighted least squares-based (IRLS) techniques to deal with outliers, have been selected for comparison purposes. The results obtained show that the proposed motion estimator can obtain, in most cases, more accurate estimates that the IRLS-based techniques.