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ICIG
2009
IEEE

Statistical Modeling of Optical Flow

8 years 12 months ago
Statistical Modeling of Optical Flow
Optical flow estimation is one of the main subjects in computer vision. Many methods developed to compute the motion fields are built using standard heuristic formulation. In this paper, however, we learn a motion model. We develop a hybrid model by combining the learnt model with Markov Random Field (MRF). And then we introduce a method based on "Radial Basis Function Neural Network" (RBF) to learn the model. When computing the displacement field, a Gaussian pyramidal down-sampling decomposition technique is employed. At each pyramidal level, we use bi-linear interpolation combined with an efficient warping technique to generate a residual image, which is then used at the finer level to compute the flow. To minimize the energy, we use two different discrete optimization methods: Graph-Cut algorithm, Tree-Reweighted Message Passing (TRW-S) algorithm. Results are demonstrated for our approach on synthetic images and fluid images from the real world.
Dongmin Ma, Véronique Prinet, Cyril Cassisa
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICIG
Authors Dongmin Ma, Véronique Prinet, Cyril Cassisa
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