Motion prediction approaches were categorized based on their degree of abstraction.
Motion prediction approaches, such as the use of the Kalman filter [9, 10] and particle filter [11], cannot be used because image changes caused by peristaltic motion appear to be random.
In addition, in this paper, we use pixel accuracy motion estimation and motion prediction, extending the current approach to quarter-pixel accuracy motion estimation and motion prediction would also be promising.
We have chosen to organize motion modeling and prediction approaches according to the kind of hypotheses they make about the modeled entities.
The loss-aware motion estimation and loss-aware motion prediction is not used in our approach, because we extend ROPE in different direction, in fact, the gain can be accumulated if both the loss-aware motion estimation and loss-aware motion prediction are applied.
This paper presents a novel, efficient approach for ranking of ground motion prediction equations based on artificial neural network (ANN).
We use a characterized source model proposed by Irikura and Miyake (2001), which is one of the most reliable approaches for broadband strong ground motion prediction, as a source model for strong ground motion simulation.
Their results encouraged the application of the approach as a supplementary tool for site-specific ground motion prediction.
KF algorithm is one of the best predictive algorithms, especially for noise processing and motion prediction.
Population prediction and motion prediction resolve those uncertainties.
