6 research outputs found
Line Based Camera Calibration In Machine Vision Dynamic Applications
The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface.10210010
Theoretical and Numerical Analysis of 3D Reconstruction Using Point and Line Incidences
We study the joint image of lines incident to points, meaning the set of
image tuples obtained from fixed cameras observing a varying 3D point-line
incidence. We prove a formula for the number of complex critical points of the
triangulation problem that aims to compute a 3D point-line incidence from noisy
images. Our formula works for an arbitrary number of images and measures the
intrinsic difficulty of this triangulation. Additionally, we conduct numerical
experiments using homotopy continuation methods, comparing different approaches
of triangulation of such incidences. In our setup, exploiting the incidence
relations gives both a faster point reconstruction and in three views more
accurate.Comment: 27 pages, 5 Figures, 3 table
Affine Structure from Line Correspondences with Uncalibrated Affine Cameras
This paper presents a linear algorithm for recovering 3D affine shape and motion from line correspondences with uncalibrated affine cameras. The algorithm requires a minimum of seven line correspondences over three views. The key idea is the introduction of a one-dimensional projective camera. This converts 3D affine reconstruction of "line directions" into 2D projective reconstruction of "points". In addition, a line-based factorisation method is also proposed to handle redundant views. Experimental results both on simulated and real image sequences validate the robustness and the accuracy of the algorithm
Affine Structure from Line Correspondences with Uncalibrated Affine Cameras
This paper presents a linear algorithm for recovering 3D affine shape and motion from line correspondences with uncalibrated affine cameras. The algorithm requires a minimum of seven line correspondences over three views. The key idea is the introduction of a one-dimensional projective camera. This converts 3D affine reconstruction of "line directions" into 2D projective reconstruction of "points". In addition, a line-based factorisation method is also proposed to handle redundant views. Experimental results both on simulated and real image sequences validate the robustness and the accuracy of the algorithm