Potential Optical Flow
نویسندگان
چکیده
Given image data of a fluid flow, the flow field, 〈u, v〉, governing the evolution of the system can be estimated using a variational approach to optical flow. Assuming that the flow field governing the advection is the symplectic gradient of a stream function or the gradient of a potential function – both falling under the category of a potential flow – it is natural to re-frame the optical flow problem to reconstruct the stream or potential function directly rather than the components of the flow individually. There are several advantages to this framework. Minimizing a functional based on the stream or potential function rather than based on the components of the flow will ensure that the computed flow is a potential flow. Next, this approach allows a more natural method for imposing scientific priors on the computed flow, via regularization of the optical flow functional. Also, this paradigm shift gives a framework – rather than an algorithm – and can be applied to nearly any existing variational optical flow technique. In this work, we develop the mathematical formulation of the potential optical flow framework and demonstrate the technique on synthetic flows that represent important dynamics for mass transport in fluid flows, as well as a flow generated by a satellite data-verified ocean model of temperature transport.
منابع مشابه
Comparison between single and double flow plane solar heaters considering gas radiation effect
ABSTRACT: In this paper, the thermal characteristics of single and double flow plane solar heaters with radiating working gas were analyzed and compared by numerical analysis for the first time. The laminar mixed convection gas flow in the heaters was numerically simulated by the CFD method using the finite volume technique. The set of governing equations included the conservation of mass, mome...
متن کاملA framework for estimating potential fluid flow from digital imagery.
Given image data of a fluid flow, the flow field, , governing the evolution of the system can be estimated using a variational approach to optical flow. Assuming that the flow field governing the advection is the symplectic gradient of a stream function or the gradient of a potential function-both falling under the category of a potential flow-it is natural to re-frame the optical flow pro...
متن کاملComputation Optical Flow Using Pipeline Architecture
Accurate estimation of motion from time-varying imagery has been a popular problem in vision studies, This information can be used in segmentation, 3D motion and shape recovery, target tracking, and other problems in scene analysis and interpretation. We have presented a dynamic image model for estimating image motion from image sequences, and have shown how the solution can be obtained from a ...
متن کاملOn the Integration of Optical Flow and Action Recognition
Most of the top performing action recognition methods use optical flow as a “black box” input. Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better. In particular, we investigate the impact of different flow algorithms and input transformation...
متن کاملSecrets in Computing Optical Flow by Convolutional Networks
Convolutional neural networks (CNNs) have been widely used over many areas in compute vision. Especially in classification. Recently, FlowNet and several works on optical estimation using CNNs shows the potential ability of CNNs in doing per-pixel regression. We proposed several CNNs network architectures that can estimate optical flow, and fully unveiled the intrinsic different between these s...
متن کامل