Can multiple views make up for lack of camera registration?

Trivedi, Harit P.

doi:10.1016/0262-8856(88)90042-x

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…4: as we add more cameras, there is a central circle which, in the 1 If the PSF causes the point to be spread across multiple pixels, one could exploit more advanced sampling techniques that achieve sub-pixel accuracy. 2 This definition is valid when n is even. For odd n, Qw(y) = sign(y) case of odd n, is not further divided and, in the case of even n, is only divided through the origin.…”

Section: Orthogonal Projectionmentioning

confidence: 99%

On the accuracy of point localisation in a circular camera-array

Ghasemi

Scholefield

Vetterli

2015

2015 IEEE International Conference on Image Processing (ICIP)

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Although many advances have been made in light-field and camera-array image processing, there is still a lack of thorough analysis of the localisation accuracy of different multi-camera systems. By considering the problem from a frame-quantisation perspective, we are able to quantify the point localisation error of circular camera configurations. Specifically, we obtain closed form expressions bounding the localisation error in terms of the parameters describing the acquisition setup.These theoretical results are independent of the localisation algorithm and thus provide fundamental limits on performance. Furthermore, the new frame-quantisation perspective is general enough to be extended to more complex camera configurations.

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Section: Orthogonal Projectionmentioning

confidence: 99%

On the accuracy of point localisation in a circular camera-array

Ghasemi

Scholefield

Vetterli

2015

2015 IEEE International Conference on Image Processing (ICIP)

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“…In [8], it is shown that they can also be directly derived from the fundamental matrix. In [21,12] some algebraic constraints on the intrinsic parameters were derived from those associated with the essential matrix.…”

Section: Introductionmentioning

confidence: 99%

An Algebraic Approach to Camera Self-Calibration

Dornaika

Chung

2001

Computer Vision and Image Understanding

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“…Hartley [4] proposed a singular-valuedecomposition method to compute the focal lengths from a pair of images if all other cameras parameters are known. Trivedi [11] tried to determine only the coordinates of the principal point of a camera. Maybank and Faugeras [8] proposed a theory of self-calibration.…”

Section: Introductionmentioning

confidence: 99%

Self-calibration of an Uncalibrated Stereo Rig from One Unknown Motion

Zhang¹,

Luong²,

Faugeras³

1994

Procedings of the British Machine Vision Conference 1994

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We address in this paper the problem of self-calibration and metric reconstruction (up to a scale) from one unknown motion of an uncalibrated stereo rig. The epipolar constraint is first formulated for two uncalibrated images. The problem then becomes one of estimating unknowns such that the discrepancy from the epipolar constraint, in terms of sum of squared distances between points and their corresponding epipolar lines, is minimized. Redundancy of the information contained in a sequence of stereo images makes this method more robust than using a sequence of monocular images. Real data have been used to test the proposed method, and the results obtained are quite good. We also show experimentally that it is very difficult to estimate precisely the coordinates of the principal points of cameras. A variation of as high as several dozen pixels in the principal point coordinates does not affect significantly the 3-D reconstruction. A theoretical analysis is provided in this paper to explain this phenomenon.

show abstract

Can multiple views make up for lack of camera registration?

Cited by 12 publications

References 4 publications

On the accuracy of point localisation in a circular camera-array

On the accuracy of point localisation in a circular camera-array

An Algebraic Approach to Camera Self-Calibration

Self-calibration of an Uncalibrated Stereo Rig from One Unknown Motion

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