Efficient illumination independent appearance-based face tracking
Image and Vision Computing
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
International Journal of Computer Vision
Bidirectional composition on Lie groups for gradient-based image alignment
IEEE Transactions 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
3D structure refinement of nonrigid surfaces through efficient image alignment
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Joint photometric and geometric image registration in the total least square sense
Pattern Recognition Letters
Generative face alignment through 2.5D active appearance models
Computer Vision and Image Understanding
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Image registration consists in estimating geometric and photometric transformations that align two images as best as possible. The direct approach consists in minimizing the discrepancy in the intensity or color of the pixels. The inverse compositional algorithm has been recently proposed by Baker et al. for the direct estimation of groupwise geometric transformations. It is efficient in that it performs several computationally expensive calculations at a pre-computation phase. Photometric transformations act on the value of the pixels. They account for effects such as lighting change. Jointly estimating geometric and photometric transformations is thus important for many tasks such as image mosaicing. We propose an algorithm to jointly estimate groupwise geometric and photometric transformations while preserving the efficient pre-computation based design of the original inverse compositional algorithm. It is called the dual inverse compositional algorithm. It uses different approximations than the simultaneous inverse compositional algorithm and handles groupwise geometric and global photometric transformations. Its name stems from the fact that it uses an inverse compositional update rule for both the geometric and the photometric transformations. We demonstrate the proposed algorithm and compare it to previous ones on simulated and real data. This shows clear improvements in computational efficiency and in terms of convergence.