Multiple view geometry in computer vision
Multiple view geometry in computer vision
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Determining Optical Flow
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Noise Estimation from a Single Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A review of recent range image registration methods with accuracy evaluation
Image and Vision Computing
Over-Parameterized Variational Optical Flow
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
Temporal prediction and spatial regularization in differential optical flow
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Calibration and reconstruction algorithms for a handheld 3D laser scanner
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
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We address the problem of maximum a posteriori (MAP) estimation of optical flow with a geometric prior from gray-value images. We estimate simultaneously the optical flow and the corresponding surface --- the structural optical flow (SOF) --- subject to three types of constraints: intensity constancy, geometric, and smoothness constraints. Our smoothness constraints restrict the unknowns to locally coincide with a set of finitely parameterized admissible functions. The geometric constraints locally enforce consistency between the optical flow and the corresponding surface. Our theory amounts to a discrete generalization of regularization defined in terms of partial derivatives. The point-wise regularizers are efficiently implemented with linear run-time complexity in the number of discretization points. We demonstrate the applicability of our method by example computations of SOF from photographs of human faces.