Sharif Digital Repository

Global motion estimation (GME) algorithms have an imperative role in object-based applications. Gradient-based GME is a well known method among these algorithms. Such algorithms require an initial value for their initialization step. Well estimation of this value plays a significant role in the accuracy of GME. This work introduces a simple but efficient technique for initial value prediction of GME. This technique employs a long-term predictor as well as global motions of previous frames. Simulations results demonstrate faster convergence and less computational complexity of the proposed method versus common presented techniques in the literature with almost same efficiency  

This paper proposes a global motion estimation method to remove unintentional camera motions which degrade the visual quality of image sequences. The proposed approach is based on combination of 2D Radon transform, 1D Fourier transform and 1D Scale transform which can accurately estimate scale, rotational and translational distortions of camera motion and is robust to internal moving objects. Our experimental results with real and synthesized videos indicate the effectiveness of our proposed method  

Global motion estimation (GME) is an important technique in image and video processing. Whereas the direct global motion estimation techniques boast reasonable precision they tend to suffer from high computational complexity. As with indirect methods, though presenting lower computational complexity they mostly exhibit lower accuracy than their direct counterparts. In this paper, the authors introduce a robust algorithm for GME with near identical accuracy and almost 50-times faster than MPEG-4 verification model (VM). This approach entails two stages in which, first, motion vector of sampled block is employed to obtain initial GME then Levenberg-Marquardt algorithm is applied to the... 

Global Motion Estimation (GME) has many important roles in numerous applications, such as video compression, image stabilization, video-object segmentation, and etc. One well-known GME method is the gradient-based technique. This method uses optimization techniques, like the Levenberg-Marquardt algorithm, to minimize estimation error. Such algorithms require an initial value for the initializing step. In this paper, we propose a simple and reliable GME structure with a new predictor. This structure uses a three-step search and a predictor for the initializing step. It is also incorporated with a fast GME method that uses pixel subsampling. This incorporation reduces the computational...