Relating vanishing points to catadioptric camera calibration

This paper presents the analysis and derivation of the geometric relation between vanishing points and camera parameters of central catadioptric camera systems. These vanishing points correspond to the three mutually orthogonal directions of 3D real world coordinate system (i.e. X, Y and Z axes). Compared to vanishing points (VPs) in the perspective projection, the advantages of VPs under central catadioptric projection are that there are normally two vanishing points for each set of parallel lines, since lines are projected to conics in the catadioptric image plane. Also, their vanishing points are usually located inside the image frame. We show that knowledge of the VPs corresponding to XYZ axes from a single image can lead to simple derivation of both intrinsic and extrinsic parameters of the central catadioptric system. This derived novel theory is demonstrated and tested on both synthetic and real data with respect to noise sensitivity

Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System

Az http://intechweb.org/ alatti "Books" fül alatt kell rákeresni a "Stereo Vision" címre és az 1. fejezetre

Single View 3D Reconstruction under an Uncalibrated Camera and an Unknown Mirror Sphere

In this paper, we develop a novel self-calibration method for single view 3D reconstruction using a mirror sphere. Unlike other mirror sphere based reconstruction methods, our method needs neither the intrinsic parameters of the camera, nor the position and radius of the sphere be known. Based on eigen decomposition of the matrix representing the conic image of the sphere and enforcing a repeated eignvalue constraint, we derive an analytical solution for recovering the focal length of the camera given its principal point. We then introduce a robust algorithm for estimating both the principal point and the focal length of the camera by minimizing the differences between focal lengths estimated from multiple images of the sphere. We also present a novel approach for estimating both the principal point and focal length of the camera in the case of just one single image of the sphere. With the estimated camera intrinsic parameters, the position(s) of the sphere can be readily retrieved from the eigen decomposition(s) and a scaled 3D reconstruction follows. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our approach. © 2016 IEEE.postprin

Tele-Autonomous control involving contact

Object localization and its application in tele-autonomous systems are studied. Two object localization algorithms are presented together with the methods of extracting several important types of object features. The first algorithm is based on line-segment to line-segment matching. Line range sensors are used to extract line-segment features from an object. The extracted features are matched to corresponding model features to compute the location of the object. The inputs of the second algorithm are not limited only to the line features. Featured points (point to point matching) and featured unit direction vectors (vector to vector matching) can also be used as the inputs of the algorithm, and there is no upper limit on the number of the features inputed. The algorithm will allow the use of redundant features to find a better solution. The algorithm uses dual number quaternions to represent the position and orientation of an object and uses the least squares optimization method to find an optimal solution for the object's location. The advantage of using this representation is that the method solves for the location estimation by minimizing a single cost function associated with the sum of the orientation and position errors and thus has a better performance on the estimation, both in accuracy and speed, than that of other similar algorithms. The difficulties when the operator is controlling a remote robot to perform manipulation tasks are also discussed. The main problems facing the operator are time delays on the signal transmission and the uncertainties of the remote environment. How object localization techniques can be used together with other techniques such as predictor display and time desynchronization to help to overcome these difficulties are then discussed

Camera calibration from a translation + planar motion

This paper addresses the problem of camera calibration by exploiting image invariants under camera/object rotation. A novel translation + planar motion is studied here. The 3 × 3 homography mapping corresponding points before and after the motion is exploited to obtain image invariants under perspective projection. The homography is found to form a "rotation conic" under different rotation angles. Apart from the imaged circular points, this conic can also be exploited to find the vanishing point of the rotation axis and this provides extra constraints for camera calibration. A square calibration pattern, which is invariant under a rotation about its center by multiples of π/2 radians, is introduced as a special instantiation of the translation + planar motion. Experiments on synthetic and real data show good precisions in calibration results.postprintThe 8th IASTED International Conference on Signal and Image Processing (SIP 2006), Honolulu, HI., 14-16 August, 2006. In Proceedings of the 8th IASTED International Conference on Signal and Image Processing, 2006, p. 195-20

Which pattern? Biasing aspects of planar calibration patterns and detection methods

This paper provides a comparative study on the use of planar patterns in the generation of control points for camera calibration. This is an important but often neglected aspect in camera calibration. Two popular checkerboard and circular dot patterns are each examined with two detection strategies for invariance to the potential bias from projective transformations and nonlinear distortions. It is theoretically and experimentally shown that circular patterns can potentially be affected by both biasing sources. Guidelines are given to control such bias. In contrast, appropriate checkerboard detection is shown to be bias free. The findings have important implications for camera calibration, indicating that well accepted methods may give poorer results than necessary if applied naively

A Self-Calibration Method of Zooming Camera

In this article we proposed a novel approach to self- calibrate a camera with variable focal length. We show that the estimation of camera’s intrinsic parameters is possible from only two points of an unknown planar scene. The projection of these points by using the projection matrices in two images only permit us to obtain a system of equations according to the camera’s intrinsic parameters . From this system we formulated a nonlinear cost function which its minimization allows us to estimate the camera’s intrinsic parameters in each view. The results on synthetic and real data justify the robustness of our method in term of reliability and convergence