Results 171 to 180 of about 369,007 (214)

Deep Fundamental Matrix Estimation

2018
We present an approach to robust estimation of fundamental matrices from noisy data contaminated by outliers. The problem is cast as a series of weighted homogeneous least-squares problems, where robust weights are estimated using deep networks. The presented formulation acts directly on putative correspondences and thus fits into standard 3D vision ...
René Ranftl, Vladlen Koltun
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Matrix Representation of Thermodynamic Fundamentals

American Journal of Physics, 1957
The use of matrices for representing fundamental thermodynamic relations is demonstrated. Maxwell's relations and other thermodynamic derivatives are readily obtained by differentiation of the matrices defined.
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Fundamental Solution Matrix

2004
Trajectories of a dynamical system, starting from a particular initial state, might evolve towards a steady state of the system. A steady state can be an equilibrium of the system but can also be a (quasi-)periodic motion. The stability of equilibria is (for the hyperbolic case) determined by the eigenvalues of the local linearization of the system ...
Remco I. Leine, Henk Nijmeijer
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Transfer-matrix fundamentals

International Journal of Mechanical Sciences, 1960
Abstract In the past ten years several authors1–11 have developed the method of transfer matrices for the vibration and stability analysis of complicated elastic systems. This paper is an attempt to generalize and unify the method, and it is hoped that as a result many further problems ‡ will fall within the scope of this method.
F. Leckie, E. Pestel
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Fundamental matrix for cameras with radial distortion

Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005
When deploying a heterogeneous camera network or when we use cheap zoom cameras like in cell-phones, it is not practical, if not impossible to off-line calibrate the radial distortion of each camera using reference objects. It is rather desirable to have an automatic procedure without strong assumptions about the scene.
João Pedro Barreto 0001, Kostas Daniilidis   +1 more
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Neural Computation of the Fundamental Matrix

2004
The fundamental matrix combines the mutual relation of the corresponding points in the two images of an observed scene. This relation, known also as an epipolar geometry, allows for a further depth reconstruction, image rectification or camera calibration.
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Fundamental Matrix Computation

2016
Two images of the same scene are related by what is called the epipolar equation. It is specified by a matrix called the fundamental matrix. By computing the fundamental matrix between two images, one can analyze the 3D structure of the scene, which we discuss in Chaps. 4 and 5.
Kenichi Kanatani, Yasuyuki Sugaya, Yasushi Kanazawa   +2 more
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A robust algorithm to estimate the fundamental matrix

Pattern Recognition Letters, 2000
Abstract A new method, the biepipole constraint algorithm, is developed to estimate the fundamental matrix (F-matrix) based on an 8-parameter model and the geometrical analysis. First, through the analysis of the new constraints, the four parameters of the F-matrix can be estimated by solving a nonlinear unconstraint optimization problem.
Chen, Z., Wu, C., Shen, P., Liu, Y., Quan, Long   +4 more
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On a fundamental property of the cross- Gramian matrix

IEEE Transactions on Circuits and Systems, 1984
Summary: The cross-Gramian matrix \(W_{c0}(T)\) for linear single-input single- output (SISO) dynamical systems has been related to the controllability and observability Gramians via \(W^ 2_{c0}(T)=W_ c(T)W_ 0(T)\) for \(T=\infty\). The result is now proved for any arbitrary time interval T, and is extended to symmetric multivariable systems which have
Fernando, K. V., Nicholson, H.
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Simultaneously Estimating the Fundamental Matrix and Homographies

IEEE Transactions on Robotics, 2009
The estimation of the fundamental matrix (FM) and/or one or more homographies between two views is of great interest for a number of computer vision and robotics tasks. We consider the joint estimation of the FM and one or more homographies. Given point matches between two views (and assuming rigid geometry of the camera-scene displacement), it is well
Pei Chen 0001, David Suter
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