Robust Parameter Estimation in Computer Vision
SIAM Review
Robust computer vision: an interdisciplinary challenge
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Motion Estimation Using Statistical Learning Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Stabilization Using Scale-Invariant Features
IV '07 Proceedings of the 11th International Conference Information Visualization
SIFT Features Tracking for Video Stabilization
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Digital image stabilization by adaptive block motion vectors filtering
IEEE Transactions on Consumer Electronics
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Video sequences acquired by a camera mounted on a hand held device or a mobile platform are affected by unwanted shakes and jitters. In this situation, the performance of video applications, such us motion segmentation and tracking, might dramatically be decreased. Several digital video stabilization approaches have been proposed to overcome this problem. However, they are mainly based on motion estimation techniques that are prone to errors, and thus affecting the stabilization performance. On the other hand, these techniques can only obtain a successfully stabilization if the intentional camera motion is smooth, since they incorrectly filter abrupt changes in the intentional motion. In this paper a novel video stabilization technique that overcomes the aforementioned problems is presented. The motion is estimated by means of a sophisticated feature-based technique that is robust to errors, which could bias the estimation. The unwanted camera motion is filtered, while the intentional motion is successfully preserved thanks to a Particle Filter framework that is able to deal with abrupt changes in the intentional motion. The obtained results confirm the effectiveness of the proposed algorithm.