IEEE Transactions on Pattern Analysis and Machine Intelligence
The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spline-Based Image Registration
International Journal of Computer Vision
A General Motion Model and Spatio-Temporal Filters forComputing Optical Flow
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable and Efficient Computation of Optical Flow
International Journal of Computer Vision
Skin and Bones: Multi-layer, Locally Affine, Optical Flow and Regularization with Transparency
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Optical flow: a curve evolution approach
IEEE Transactions on Image Processing
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
Image Registration under Varying Illumination: Hyper-Demons Algorithm
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A study on local photometric models and their application to robust tracking
Computer Vision and Image Understanding
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In this paper, we present a new motion estimation algorithm that provides accurate optical flow computation under non-uniform brightness variations. The proposed algorithm is based on a regularization formulation that minimizes a combination of a modified data constraint energy and a smoothness measure all over the image domain. The data constraint is derived from the conservation of the Laplacian-of-Gaussian (LoG) filtered image function, which alleviates the problem with the traditional brightness constancy assumption under non-uniform illumination variations. The resulting energy minimization is accomplished by an incomplete Cholesky preconditioned conjugate gradient algorithm. Comparisons of experimental results on benchmarking image sequences by using the proposed algorithm and some of the best existing methods are given to demonstrate its superior performance.