Results 251 to 260 of about 4,693,472 (296)

Robust estimation of the fundamental matrix

2010 IEEE International Conference on Image Processing, 2010
Most approaches to estimate the fundamental matrix assume a Gaussian distribution in the errors in view of mathematical tractability. However, this assumption is violated if the distribution computed is not normal. In this paper we propose a robust approach of estimating the fundamental matrix which does not rely on the Gaussian assumption.
Huiyu Zhou 0001, Gerald Schaefer
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A stability analysis of the Fundamental matrix

1994
The Fundamental matrix is a key concept when working with uncalibrated images and multiple viewpoints. It contains all the available geometric information and enables to recover the epipolar geometry from uncalibrated perspective views. This paper is about a stability analysis for the Fundamental matrix.
Quang-Tuan Luong, Olivier D. Faugeras
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On Accurate and Robust Estimation of Fundamental Matrix

Procedings of the British Machine Vision Conference 1996, 1996
This paper is concerned with accurate and robust estimation of the fundamental matrix. We show that, given certain conditions, a basic linear algorithm can yield excellent accuracy, in some cases two orders of magnitude better than sophisticated algorithms.
Bober, M, Georgis, N, Kittler, J
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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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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 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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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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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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