The aim of this work is to propose a model for computing the optical flow in a sequence of images. We introduce a new temporal regularizer that is suitable for large displacements. We propose to decouple the spatial and temporal regularizations to avoid an incongruous formulation. For the spatial regularization we use the Nagel-Enkelmann operator and a newly designed temporal regularization. Our model is based on an energy functional that yields a partial differential equation (PDE). This PDE is embedded into a multipyramidal strategy to recover large displacements. A gradient descent technique is applied at each scale to reach the minimum. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Salgado, A., & Sánchez, J. (2007). Temporal constraints in large optical flow estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4739 LNCS, pp. 709–716). Springer Verlag. https://doi.org/10.1007/978-3-540-75867-9_89
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