O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proc. 5th European Conf. on Computer Vision, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....views of seven lines[6] Another area of application is vision for planar motion. It is shown that ordinary vision (two dimensional retina) can be reduced to that of one dimensional cameras if the motion is planar, i.e. if the camera is rotating and translating in one specific plane only, cf. [14]. In another paper the planar motion is used for auto calibration [3] A typical example is the case where a camera is mounted on a vehicle that moves on a flat floor or flat road. 1.2 One dimensional vision In one dimensional vision one deals with projections of a plane onto a line or circle, ....

O. D. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proc. 5th European Conf. on Computer Vision, Freiburg, Germany, pages 36--52. Springer-Verlag, 1998.

....problems for lines, 12, 3] Another area of application is vision for planar motion. It is shown that ordinary vision (two dimensional retina) can be reduced to that of one dimensional cameras if the motion is planar, i.e. if the camera is rotating and translating in one specific plane only, cf. [7]. In another paper the planar motion is used for auto calibration [1] A typical example is the case where a camera is mounted on a vehicle that moves on a flat plane or flat road. Our personal motivation, however, stems from the autonomous guided vehicles, called AGV, which are important ....

O. D. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proc. 5th European Conf. on Computer Vision, Freiburg, Germany, pages 36--52. Springer-Verlag, 1998.

....a ne cameras can be reduced to the structure and motion problem for 1D cameras. Another area of application is vision for planar motion. The ordinary 2D retina vision can be reduced to that of 1D cameras if the motion is planar, i.e. the camera is rotating and translating in one speci c plane, cf. [1, 10]. A typical example is the case where a camera is mounted on a vehicle that moves on a at plane. The 1D camera may also serve as a good model for the navigation system in laser guided vehicles, called LGV. The vision system uses strips of re ector tape which are put on walls or objects along the ....

O. D. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In European Conf. Computer Vision, volume I, pages 3652, Freiburg, Germany, 1998.

....at cases in which the scene is the ground plane, and the rotation axis is vertical either because the camera is on a pan tilt unit (which usually have vertical pan axes) or because the camera moves along the plane, or both. Planar motion has been extensively studied by Armstrong and others [2, 7, 18], but the various planar motion calibration methods proposed are intended for general scenes and use the fundamental matrix. Our method bears more re semblance to other planar scene algorithms, and also in part to rotating camera algorithms, although they use the infinite homography, which ....

O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1D projective camera and its application to the self-calibration of a 2D projective camera. In Proc. 5th European Conf on Computer Vision, Freiburg, volume I, pages 362, 1998.

....to present a benchmark system for selecting a robust detector. It is well known that feature points detectors find salient points in natural images. These points are often located near corners and edges of the image. Feature points were first developed for computer vision and reconstruction [6][17] but they are also employed in data base retrieval as a descriptor of the image [29] 14] We focus our study on three detectors that are commonly used in pattern recognition and vision systems. A. Harris Detector The Harris and Stephens detector was developed for 3 D reconstruction [19] This ....

O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proceedings of the 5th European Conference on Computer Vision, Freiburg, Germany, pages 36--52, June 1998.

....(RESOLV) was carried out on this theme, though using a laser range scanner as the principal acquisition device. Planar motion for a limited number of views has been investigated in an uncalibrated framework by Beardsley and Zisserman [3] 2 views) and Armstrong et al. 1] and Faugeras et al. [10] (3 views) The novelty in the work described here is the reduction to a one parameter search. The paper is organized as follows: section 3 describes the motion determination. In the case of planar motion there are only three parameters that must be determined for each frame: the (x; y) position ....

Faugeras O. D., Quan L., and Sturm P. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2D projective camera. In ECCV, pages 36-52, 1998.

....cameras can be reduced to the structure and motion problem for 1D cameras. Another area of application is vision for planar motion. The ordinary 2D retina vision can be reduced to that of 1D cameras if the motion is planar, i.e. the camera is rotating and translating in one specific plane, cf. [1, 7]. A typical example is the case where a camera is mounted on a vehicle that moves on a flat plane. The 1D camera may also serve as a good model for the This work has been done within the IST project 1999 10736, VISIRE and the Swedish Research Council, project 221 2000 476. b Reflector Angle ....

O. D. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In European Conf. Computer Vision, volume I, pages 36--52, Freiburg, Germany, 1998.

....out and preliminary experiments with synthetic and real data show the reliability and robustness of the approximation under a wide range of conditions. 1 Introduction The trifocal tensor is an important tool for several tasks in Computer Vision, like reconstruction [1, 14] self calibration [6] and motion segmentation [17] and its computation is still a research topic [5, 18] The linear algorithms [10, 9] have simple conception and execution, but they do not take the appropriate constraints into account. This results in a poor behaviour when the number of point or line correspondences ....

.... squares in image c) 6 Conclusions The trifocal tensor plays for trinocular rigs the same role as the fundamental matrix plays for stereo rigs, allowing recovery of structure apart from a 3D homography [9] It is an effective tool for solving problems in several major topics of Computer Vision [1, 14, 17, 6]. The major contribution of this paper is the introduction of an affine trifocal tensor.This entity is connected to the general trifocal tensor in the same way that the affine fundamental matrix [16] and the affine camera [11] are connected to the general fundamental matrix and the projective ....

O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proc. 5th European Conf. on Computer Vision, 1998.

....the camera positions should be equal to those returned by the calibration method. Of course, our validation procedure is adequate only if the camera calibration method can be trusted. For cameras with moderate fields of view, disregarding lens distortion, calibration methods can be found in [6, 7, 3] for fixed intrinsic parameters and in [17, 12, 16] for varying parameters. With controlled camera motion, such as no or known camera translations, these calibration methods can be made more robust [15, 13, 2, 9] When lens distortion is present, calibration is cast as an optimization problem ....

O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1-d projective camera and its application to the self-calibration of a 2-d projective camera. In ECCV98, pages 36--52, 1998.

....can be found in the works of Brooks [6] Kriegman [15] Kak [14] Lebegue [17] and Betke [4] It should be mentioned here that the vertical line formulation is equivalent with using 1D projections of points (a horizontal slice of the environment) in order to recover a 2D map. Faugeras et al. [9] formulated and solved the problem of 2D recovery from 1D projection in a projective geometric framework without knowledge of the calibration of the camera. In the case where the positions of the landmarks are known, the problem is reduced to simple navigation and numerous approaches have been ....

O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proc. Fifth European Conference on Computer Vision, pages 36--52, Freiburg, Germany, 1998.

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O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proceedings of ECCV, pages 36--52, 1998.

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O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proceedings of the 5th European Conference on Computer Vision, Freiburg, Germany, pages 36--52, June 1998.

....exact linear algorithm can be used for 1D camera self calibration. The only constraint is that the motion of the 2D camera should be restricted to planar motions. The other applications, including 2D affine camera calibration, are also briefly discussed. Part of this work was also presented in [10]. The paper is organised as follows. In Section 2, we review the 1D projective camera and its trifocal tensor. Then, an efficient estimation of the trifocal tensor is discussed in Section 3. The theory of self calibration of a 1D camera is introduced and developed in Section 4. After pointing out ....

O. Faugeras and L. Quan and P. Sturm. Self-calibration of a 1D projective camera and its application to the self-calibration of a 2D projective camera. In European Conference on Computer Vision, Freiburg, Germany, June 1998.

....reconstruction [7, 10, 17, 29, 11, 8] of which Trigg s formulation based on absolute quadric [29] has been the most significant. More specialized methods exist for particular types of motion, particular scenes and simplified calibration models [8, 31, 1, 18, 30] Affine cameras [22] 1D cameras [6] and Stereo heads [33, 12] can also be autocalibrated. Solutions are still in theory possible if some of the intrinsic parameters are allowed to vary [10, 17] The numerical conditioning of classical autocalibration is historically delicate, although recent algorithms have improved the ....

....solution. Sturm [27, 28] has given a catalogue of these. In particular, at least 3 views, some translation and some rotation about at least two non aligned axes are required. Further work on the degeneracies have also been studied in [2, 13] Most of the materials presented in this paper come from [29, 30, 22, 6]. 2 Preliminaries 2.1 Notation and Basics Throughout the paper, vectors and matrices are respectively denoted by lower and upper case bold letters. We use P for image projections and H for inter image homographies; K, for upper triangular camera calibration and C = K 1 for its inverse; # for ....

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O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the selfcalibration of a 2d projective camera. ECCV'98.

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O. Faugeras, L. Quan, and P. Sturm. Self-calibration of a 1d projective camera and its application to the self-calibration of a 2d projective camera. In Proc. 5th European Conf. on Computer Vision, 1998.

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O. Faugeras, L. Quan, and P. Sturm, Self-calibration of a 1D projective camera and its application to the self-calibration of a 2-D projective camera, Proc.

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O. Faugeras, L. Quan and P. Strum, \Self-calibration of a 1D Projective Camera and its Application to the Self-Calibration of a 2-D Projective Camera", in Proc. ECCV'98.

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