Solving the Camera Intrinsic Parameters with the Positive Tri-prism Based on the Circular Points

Zhao, Yue; Hu, Xiaohua; Lv, Xiaodan; Wang, Hui

doi:10.3923/itj.2012.926.930

“…The methods of autocalibration of the cameras are based on the estimation of the camera's parameters but without any knowledge a priori on the stage. Among these methods, we have those treated in ([4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [20], [21], [22], [23], [24], [25], [26]). The cost functions of these methods are generally non-linear, and they are formulate according to the invariants in the images and camera parameters.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of three-dimensional scenes from a sequence of images of which only one is calibrated

Karim

¹

,

Akkad²,

Satori

³

et al. 2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

0

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In this work, we propose a method for reconstructing three-dimensional scenes from a sequence of images which only one image is calibrate. The system is initialized by a calibrated image. The projection matrices of the other images in the sequence are estimated from the first projection matrix (obtained by the calibration of the camera), and the matching between the images is determined by solving a system of linear equations. The intrinsic and extrinsic parameters of the camera used are estimated from the Cholesky decomposition of the projection matrix. Finally, obtaining the 3D scene is based on the Euclidean reconstruction of the detected and matched control points in the pairs of images.

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“…Self-calibration of the cameras is the main step to obtain three-dimensional coordinates of points from matches between pairs of images. Several methods of camera self-calibration with constant intrinsic parameters [5][6][7][8][9][10][11][12][13][14] and those with varying intrinsic parameters [15][16][17][18][19][20][21][22][23][24][25] are treated in this area.…”

Section: Introductionmentioning

confidence: 99%

Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene

Satouri¹,

EL²,

TAIRI³

et al. 2016

ijacsa

3

0

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Abstract-In the present paper, we will propose a new and robust method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used to determine the projection matrices. The present method is based on the formulation of a non linear cost function from the determination of a relationship between two points of the scene with their opposite relative to the axis of abscise and their projections in the image planes. The resolution of this function with genetic algorithm enables us to estimate the intrinsic parameters of different cameras. The important of our approach reside in the use of a single pair of images which provides fewer equations, simplifies the mathematical complexities and minimizes the execution time of the application, the use of the data of the first image only without the use of the data of the second image, the use of any camera which makes the intrinsic parameters variable not constant and the use of a 3D scene reduces the planarity constraints. The experimental results on synthetic and real data prove the performance and robustness of our approach.

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Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene

Akkad

¹

,

Merras

²

,

Saaidi

³

et al. 2013

View full text Add to dashboard Cite

Abstract-In the present paper, we will propose a new and robust method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used to determine the projection matrices. The present method is based on the formulation of a non linear cost function from the determination of a relationship between two points of the scene with their opposite relative to the axis of abscise and their projections in the image planes. The resolution of this function with genetic algorithm enables us to estimate the intrinsic parameters of different cameras. The important of our approach reside in the use of a single pair of images which provides fewer equations, simplifies the mathematical complexities and minimizes the execution time of the application, the use of the data of the first image only without the use of the data of the second image, the use of any camera which makes the intrinsic parameters variable not constant and the use of a 3D scene reduces the planarity constraints. The experimental results on synthetic and real data prove the performance and robustness of our approach.

show abstract

Solving the Camera Intrinsic Parameters with the Positive Tri-prism Based on the Circular Points

Cited by 8 publications

References 10 publications

Reconstruction of three-dimensional scenes from a sequence of images of which only one is calibrated

Reconstruction of three-dimensional scenes from a sequence of images of which only one is calibrated

Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene

Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene

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