IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Parameter Simultaneous Estimation on Area-Based Matching
International Journal of Computer Vision
Global parametric image alignment via high-order approximation
Computer Vision and Image Understanding
Detection and compression of moving objects based on new panoramic image modeling
Image and Vision Computing
Global Intensity Correction in Dynamic Scenes
International Journal of Computer Vision
Geometric calibration of digital cameras through multi-view rectification
Image and Vision Computing
Geometric registration of images with arbitrarily-shaped local intensity variations from shadows
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Geometric image registration under locally variant illuminations using Huber M-estimator
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Fast and accurate global motion compensation
Pattern Recognition
Global motion estimation: feature-based, featureless, or both ?!
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
New panoramic image generation based on modeling of vignetting and illumination effects
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
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Methods for estimating motion in video sequences that are based on the optical flow equation (OFE) assume that the scene illumination is uniform and that the imaging optics are ideal. When these assumptions are appropriate, these methods can be very accurate, but when they are not, the accuracy of the motion field drops off accordingly. This paper extends the models upon which the OFE methods are based to include irregular, time-varying illumination models and models for imperfect optics that introduce vignetting, gamma, and geometric warping, such as are likely to be found with inexpensive PC cameras. The resulting optimization framework estimates the motion parameters, illumination parameters, and camera parameters simultaneously. In some cases these models can lead to nonlinear equations which must be solved iteratively; in other cases, the resulting optimization problem is linear. For the former case an efficient, hierarchical, iterative framework is provided that can be used to implement the motion estimator.