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

Akkad, Nabil El; Merras, Mostafa; Saaidi, Abderrahim; Satori, Khalid

doi:10.1007/s00371-013-0877-2

Cited by 31 publications

(10 citation statements)

References 21 publications

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“…It is based on quasi-affine reconstruction, after reconstruction, the homography of the plane at infinity is determined, and used with constraints on the image of the absolute conic to estimate the intrinsic camera parameters. In [25], the author proposed a self-calibration method with variable intrinsic parameters from a sequence of images of a 3D unknown object. The projection of two points of the 3D scene in the image planes is used with the fundamental matrix to determine the matrices of the projection of the object in images.…”

Section: Existing Workmentioning

confidence: 99%

Accurate Self-calibration of Camera with Variable Intrinsic Parameters from Unknown 3D Scene

Batteoui

Saaidi

Satori

2015

3D Res

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In this article we present a practical selfcalibration method of camera having variable intrinsic parameters. This method is an extension of a method which we have already published. The main idea of our method addressed in this paper is the use of interest points automatically detected in two images of an unknown 3D scene to self-calibrate the camera. These interest points are the projections of the vertices of unknown 3D parallelograms (each triplet of interest points is the projections of three vertices of a unique 3D parallelogram). The projection of the vertices of parallelograms in the two image planes of the camera allows us to obtain two equations based on the intrinsic camera parameters. Seeing that our camera can change its intrinsic parameters from a view to another so the number of unknowns is ten hence the need to use at least five parallelograms in the scene to obtain other equations in function of intrinsic camera parameters. The paper tests the performance of the proposed approach on the both synthetic and real data.

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Section: Existing Workmentioning

confidence: 99%

Accurate Self-calibration of Camera with Variable Intrinsic Parameters from Unknown 3D Scene

Batteoui

Saaidi

Satori

2015

3D Res

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“…Stereoscopic matching is an area of research that affects many areas of computer vision: Estimation of camera parameters [1], 3D reconstruction [2], pattern recognition [3], etc. It consists in finding in two images of a same scene taken from different viewpoints, the pairs of pixels which are the projections of the same point of the 3D scene.…”

Section: Introductionmentioning

confidence: 99%

Images Matching Using Voronoï Regions Propagation

Laraqui

Saaidi

Mouhib

et al. 2015

3D Res

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This paper presents a robust dense matching algorithm based on a geometric approach Voronoï. Feature points are matched and used to divide the images (left and right) to Voronoï regions. A left Voronoï region corresponds with its right counterpart, if the sites of the two regions constitute a true matching (seed).The two regions have the same number of points, the same shape and are highly correlated. The points of the Voronoï regions which satisfy all these criteria serve as new seeds for the next iteration. The originality of our approach lies in the fact that the segmentation strategy of the image is based on the distance between the pixels and not on the intensity (colour). The results obtained from test images show that our method is robust and reduces errors due to problems of ambiguity between pixels belonging to areas with low and/or repetitive textures.It also enables us to overcome the problems of occlusions and depth discontinuities.

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“…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

Akkad²,

Satori

et al. 2019

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

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

Cited by 31 publications

References 21 publications

Accurate Self-calibration of Camera with Variable Intrinsic Parameters from Unknown 3D Scene

Accurate Self-calibration of Camera with Variable Intrinsic Parameters from Unknown 3D Scene

Images Matching Using Voronoï Regions Propagation

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

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