Bi-tangent line based approach for multi-camera calibration using spheres

Yu, Jian Wu; Da, Feipeng

doi:10.1364/josaa.35.000221

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…In the descriptions of the experiments, the calibration algorithms based on the images of the circular points and orthogonal vanishing points are denoted as ICP and OVP, respectively. The methods using the bi-tangent lines of the projection conics [33], semidefinite programming [35], the common pole and polar with respect to two sphere images [20], grid spherical images [17], and homography deduced by conic features [40] are denoted as BTL, SDP, CPP, GSI, and HCF, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, after recovering the intrinsic parameters K, image o i of the sphere centre can be estimated [33], [34]:…”

Section: Estimation Of Extrinsic Parametersmentioning

confidence: 99%

“…The final results can be obtained using the Levenberg-Marquardt algorithm [33] with the initial values k 1 = 0 and k 2 = 0.…”

Section: Determination Of the Distortion Coefficientmentioning

confidence: 99%

“…The three vertices w 1 , w 2 , w 3 of a common self-polar triangle are located on line l w , and vertex w 2 (w 3 ) is the common tangent (double) point of two conics c 1 , c 2 . In this case, there is no calibration method in the literature [19], [33] that can be utilized to distinguish the vanishing point and the projection of the sphere centre. Due to lack of space, we demonstrate only a degenerate case in which no common self-polar triangles exist for conics c 1 , c 2 in this paper.…”

Section: G Singularitiesmentioning

confidence: 99%

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Camera Calibration Using Projective Invariants of Sphere Images

2020

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Two novel linear camera calibration algorithms were investigated in this study. In general, an occluding contour circle of a sphere and the optical centre of a camera form a right circular cone, and the generalised eigenvectors of two right circular cones encode an infinity point that lies on two support planes containing the contour circle. In the image plane, a vanishing line can be determined by connecting two vanishing points. Furthermore, the direction of the diameter passing through the infinity point is orthogonal to the direction of its conjugate diameter; thus, a set of orthogonal vanishing points can be computed. The imaged circular points or orthogonal vanishing points provide constraints that enable the intrinsic parameters to be recovered completely. The results of simulations and comparison data obtained from actual experiments confirm the effectiveness and feasibility of the proposed algorithms. INDEX TERMS Camera calibration, conjugate diameters, generalized eigenvalue, right circular cone, vanishing point.

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Section: Methodsmentioning

confidence: 99%

“…In addition, after recovering the intrinsic parameters K, image o i of the sphere centre can be estimated [33], [34]:…”

Section: Estimation Of Extrinsic Parametersmentioning

confidence: 99%

“…The final results can be obtained using the Levenberg-Marquardt algorithm [33] with the initial values k 1 = 0 and k 2 = 0.…”

Section: Determination Of the Distortion Coefficientmentioning

confidence: 99%

Section: G Singularitiesmentioning

confidence: 99%

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Camera Calibration Using Projective Invariants of Sphere Images

2020

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“…In particular, there may be a case in which a circle wholly or partially covers another when the camera is in a critical position. For the contour with partial occlusion, a fast and effective method [15] was adopted to detect the circle images automatically.…”

Section: B Experiments Using Real Imagesmentioning

confidence: 99%

Two Separate Circles With Same-Radius: Projective Geometric Properties and Applicability in Camera Calibration

2020

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In the domain of computer vision, camera calibration is a key step in recovering the twodimensional Euclidean structure. Circles are considered important image features similar to points, lines, and conics. In this paper, a novel linear calibration method is proposed using two separate same-radius (SSR) circles as the calibration pattern. We show that the distinct pair of dual circles encodes three lines, two of which are parallel to each other and perpendicular to the remaining line. When any two coplanar or parallel circles degenerate to SSR circles, a solution can be found to recover another pair of parallel lines based on the geometric properties of the SSR circles. Using the vanishing points obtained as the key helper for determining the imaged circular points and the orthogonal vanishing points, we deduce the constraints on the image of the absolute conic (IAC) and then employ it for complete camera calibration. Furthermore, a closed-form solution for the extrinsic parameters can be obtained based on the projective invariance of the conic dual to the circular points. Evaluations based on simulated and real data confirmed the effectiveness and feasibility of the proposed algorithms. INDEX TERMS Camera calibration, conic dual, image of circular points, parallel lines, SSR circles.

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A multimedia stereo calibration algorithm based on rectangular pyramidal method used to aid visual navigation of ALVs under low illumination

Zhou

2019

Multimed Tools Appl

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Bi-tangent line based approach for multi-camera calibration using spheres

Cited by 15 publications

References 16 publications

Camera Calibration Using Projective Invariants of Sphere Images

Camera Calibration Using Projective Invariants of Sphere Images

Two Separate Circles With Same-Radius: Projective Geometric Properties and Applicability in Camera Calibration

A multimedia stereo calibration algorithm based on rectangular pyramidal method used to aid visual navigation of ALVs under low illumination

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