| Abstract: |
| The optical flow problem consists in determining the motion, or more exactly the velocity field, of an object function representing the brightness pattern in an image. The optical flow problem is reduced to an optimal control problem governed by a linear parabolic equation having the unknown velocity field (the optical flow) as drift term. This model is derived from a new assumption, that is, the brightness intensity is conserved on a moving pattern driven by a Gaussian stochastic process. The optimality conditions are deduced by a passage to the limit technique in an approximating optimal control problem introduced for a regularization purpose. Finally, the controller uniqueness is addressed. This optical flow estimation solution can be applied successfully in AI and CV-based domains like the video object detection and tracking. This is a joint work with V. Barbu. |
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