Content-preserving warps for 3D video stabilization

By Feng Liu, Michael Gleicher, Hailin Jin, Aseem Agarwala. ACM Transactions on Graphics, 2009

605 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/208048c39a9a627bb3441cbb2b7681b0c4bddcd2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Auto-directed video stabilization with robust L1 optimal camera paths (2011, methodology): “Related work: Current stabilization approaches employ key-point feature tracking and linear motion estimation in the form of 2D transformations, or use Structure from Motion (SfM) to estimate the original camera path.”
Parallax-Tolerant Image Stitching (2014, methodology): “Various content-preserving warping methods have been used in applications, such as video stabilization [14] and image and video retargeting [25, 24].”
Video magnification in presence of large motions (2015, methodology): “In the special case of camera motion, it might be possible to apply video stabilization as a preprocess to remove the undesirable large handshake, e.g. [8, 3, 9, 10], before magniﬁcation.”
Nonlinear disparity mapping for stereoscopic 3D (2010, methodology): “Recently, methods based on warping have shown to be powerful tools for complex operations on images and video which preserve the realism of the original input, including camera stabilization [Liu et al. 2009], optimizing image content [Carroll et al.”
As-Projective-As-Possible Image Stitching with Moving DLT (2013, methodology): “Additionally, for CPW, it appears that enforcing rigidity has perturbed the effects of the pre-warping by a global homography.”
Subspace video stabilization (2011, methodology): “(4) Warp the input video with the guidance of the new feature trajectories using content-preserving warps [Liu et al. 2009].”
First-person hyper-lapse videos (2014, methodology): “Most previous methods employ path smoothing algorithms [Snavely et al. 2008; Liu et al. 2009; Liu et al. 2011; Goldstein and Fattal 2012; Liu et al. 2013].”