Image-based view synthesis by combining trilinear tensors and learning techniques
VRST '97 Proceedings of the ACM symposium on Virtual reality software and technology
Threading Fundamental Matrices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel View Synthesis by Cascading Trilinear Tensors
IEEE Transactions on Visualization and Computer Graphics
Varying Focal Length Self-Calibration and Pose Estimation from Two Images
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
FIR filtering based image stabilization mechanism for mobile video appliances
CIS'04 Proceedings of the First international conference on Computational and Information Science
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Computing camera rotation from image sequences can be used for image stabilization, and when the camera rotation is known the computation of translation and scene structure are much simplified as well. A robust approach for recovering camera rotation is presented, which does not assume any specific scene structure (e.g. no planar surface is required), and which avoids prior computation of the epipole. Given two images taken from two different viewing positions, the rotation matrix between the images can be computed from any three homgraphy matrices. The homographies are computed using the trilinear tensor which describes the relations between the projections of a 3D point into three images. The entire computation is linear for small angles, and is therefore fast and stable. Iterating the linear computation can then be used to recover larger rotations as well.