Scientific Computing
Handbook of Image and Video Processing
Handbook of Image and Video Processing
Multisensor Image Registration via Implicit Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient local transformation estimation using Lie operators
Information Sciences: an International Journal
Acquisition of translational motion by the parallel trinocular
Information Sciences: an International Journal
Video model for dynamic objects
Information Sciences: an International Journal
Efficient, robust, and fast global motion estimation for video coding
IEEE Transactions on Image Processing
New adaptive pixel decimation for block motion vector estimation
IEEE Transactions on Circuits and Systems for Video Technology
Global motion parameters estimation using a fast and robust algorithm
IEEE Transactions on Circuits and Systems for Video Technology
Fast gradient methods based on global motion estimation for video compression
IEEE Transactions on Circuits and Systems for Video Technology
A hierarchical N-Queen decimation lattice and hardware architecture for motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Integration of Digital Stabilizer With Video Codec for Digital Video Cameras
IEEE Transactions on Circuits and Systems for Video Technology
New fast algorithms for the estimation of block motion vectors
IEEE Transactions on Circuits and Systems for Video Technology
Multimedia Tools and Applications
A lattice-based neuro-computing methodology for real-time human action recognition
Information Sciences: an International Journal
Community detection for hierarchical image segmentation
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
Sprite generation using sprite fusion
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Information Sciences: an International Journal
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Global motion generally describes the motion of a camera, although it may comprise motions of large objects. Global motions are often modeled by parametric transformations of two-dimensional images. The process of estimating the motions parameters is called global motion estimation (GME). GME is widely employed in many applications such as video coding, image stabilization and super-resolution. To estimate global motion parameters, the Levenburg-Marquardt algorithm (LMA) is typically used to minimize an objective function iteratively. Since the region of support for the global motion representation consists of the entire image frame, the minimization process tends to be very expensive computationally by involving all the pixels within an image frame. In order to significantly reduce the computational complexity of the LMA, we proposed to select only a small subset of the pixels for estimating the motion parameters, based on several subsampling patterns and their combinations. Simulation results demonstrated that the proposed method could speed up the conventional GME approach by over ten times, with only a very slight loss (less than 0.1dB) in estimation accuracy. The proposed method was also found to outperform several state-of-the-art fast GME methods in terms of the speed/accuracy tradeoffs.