PREDICTION OF HYDROLOGIC SYSTEM BY KALMAN FILTERING

Extended Kalman Filtering by

Digital Video Stabilization System by Adaptive Fuzzy Kalman Filtering

Digital video stabilization (DVS) allows acquiring video sequences without disturbing jerkiness, removing unwanted camera movements. A good DVS should remove the unwanted camera movements while maintains the intentional camera movements. In this article, we propose a novel DVS algorithm that compensates the camera jitters applying an adaptive fuzzy filter on the global motion of video frames. T...

Kalman Filtering

Consider the following state space model (signal and observation model). Y t = H t X t + W t , W t ∼ N (0, R) (1) X t = F t X t−1 + U t , U t ∼ N (0, Q) (2)

Kalman Filtering Motion Prediction Forrecursive Spatio - Temporal Segmentation

In the framework of computer vision, the spatio-temporal se-gmentation procedure plays a central role. It aims at identifying in the input image, semantically meaningful features that are relevant for the problem at hand. In this paper, these features are selected to be the objects forming the scene. The objects are deened by their properties of temporal and spatial coherence through the video ...

Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing and Estimation

Prediction, estimation, and smoothing are fundamental to signal processing. To perform these interrelated tasks given noisy data, we form a time series model of the process that generates the data. Taking noise in the system explicitly into account, maximumlikelihood and Kalman frameworks are discussed which involve the dual process of estimating both the model parameters and the underlying sta...