Proceedings Article10.1109/WVM.1989.47114
Optimal motion estimation
Minas E. Spetsakis,John Aloimonos +1 more
- 20 Mar 1989
- pp 229-237
51
TL;DR: It is shown that some of the difficulties inherent in the two-frame approach disappear when redundancy in the data is introduced, and the authors present two efficient ways to approximate the problem.
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Abstract: The problem of using feature correspondences to recover the structure and 3D motion of a moving object from its successive images is analyzed. They formulate the problem as a quadratic minimization problem with a nonlinear constraint. Then they derive the condition for the solution to be optimal under the assumption of Gaussian noise in the input, in the maximum-likelihood-principle sense. The authors present two efficient ways to approximate it and discuss some inherent limitations of the structure-from-motion problem when two frames are used that should be taken into account in robotics applications that involve dynamic imagery. Finally, it is shown that some of the difficulties inherent in the two-frame approach disappear when redundancy in the data is introduced. This is concluded from experiments using a structure-from-motion algorithm that is based on multiple frames and uses only the rigidity assumption. >
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Citations
Shape and motion from image streams under orthography: a factorization method
Carlo Tomasi,Takeo Kanade +1 more
TL;DR: In this paper, the singular value decomposition (SVDC) technique is used to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively, and two of the three translation components are computed in a preprocessing stage.
Shape and Motion from Image Streams: a Factorization Method: Full Report on the Orthographic Case
Carlo Tomasi,Takeo Kanade +1 more
- 01 Mar 1992
TL;DR: In this article, the singular value decomposition (SVDC) is used to factor the measurement matrix into two matrices, which represent object shape and camera motion, respectively.
Shape and motion from image streams: a factorization method
TL;DR: In this paper, a method for estimating the 3D shape of objects and the motion of the camera from a stream of images is proposed, based on the Singular Value Decomposition.
361
Flying Fast and Low Among Obstacles: Methodology and Experiments
TL;DR: A method of collision avoidance that can be used in three dimensions in much the same way as autonomous ground vehicles that navigate over unexplored terrain is developed and results are reported with an autonomous helicopter that operates at low elevations in uncharted environments.
Optimal structure from motion: local ambiguities and global estimates
Stefano Soatto,Roger W. Brockett +1 more
- 23 Jun 1998
TL;DR: An analysis of SFM is presented which results in algorithms that are provably convergent and provably optimal with respect to a chosen norm, and describes the sensitivity of the algorithm using the structure of Fisher's Information matrix.
143
References
Determining optical flow
TL;DR: In this paper, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
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•Book
Determining optical flow
Berthold K. P. Horn,Brian G. Schunck +1 more
- 03 Jan 1992
TL;DR: An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences and is robust in that it can handle image sequences that are quantified rather coarsely in space and time.
Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surfaces
Roger Y. Tsai,Thomas S. Huang +1 more
TL;DR: In this article, it was shown that seven point correspondences are sufficient to uniquely determine from two perspective views the three-dimensional motion parameters (within a scale factor for the translations) of a rigid object with curved surfaces.
Displacement vectors derived from second-order intensity variations in image sequences
TL;DR: A local approach by minimization of the squared differences between a second-order Taylor expansion of gray values from one frame and the observed gray values within the same window from the next frame appears to be an interesting model for the local computation of optical flow.
502
Inherent ambiguities in recovering 3-D motion and structure from a noisy flow field
TL;DR: Two problems which may arise due to the presence of noise in the flow field are examined and constraints and parameters which can be recovered even in ambiguous situations are presented.
297