Robust incremental optical flow
Robust incremental optical flow
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
EURASIP Journal on Advances in Signal Processing
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Efficient, robust, and fast global motion estimation for video coding
IEEE Transactions on Image Processing
Robust estimation approach for blind denoising
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Global motion estimation from coarsely sampled motion vector field and the applications
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
Global motion estimation (GME) is a vital part of many video compression and computer vision applications. However, the large moving foreground objects that are present in many video scenes make the task of GME more challenging. In this paper, we propose an automatic, efficient, and robust approach for GME that addresses the issue of large foreground objects. The proposed GME algorithm is based on two key ideas: a new clustering technique, to automate the initial segmentation of background and foreground blocks, and a modified Lorentzian estimator, to reduce the impact of any remaining foreground blocks on the GME process. We also apply an up-sampling technique to the estimated motion parameters to remove any errors caused by under-sampling during the warping process. These ideas provide a significant improvement in performance when combined into a common framework. Simulation results and analyses demonstrate the improved performance of our proposed algorithm over other state-of-the-art methods.