Automatic Calibration and Reconstruction for Active Vision by Beiwei Zhang

By Beiwei Zhang

In this ebook, the layout of 2 new planar styles for digital camera calibration of intrinsic parameters is addressed and a line-based strategy for distortion correction is advised. The dynamic calibration of based gentle platforms, which include a digital camera and a projector can also be handled. additionally, the 3D Euclidean reconstruction through the use of the image-to-world transformation is investigated. finally, linear calibration algorithms for the catadioptric digital camera are thought of, and the homographic matrix and primary matrix are commonly studied. In those equipment, analytic suggestions are supplied for the computational potency and redundancy within the information could be simply included to enhance reliability of the estimations. This quantity will as a result turn out invaluable and sensible instrument for researchers and practioners operating in photograph processing and machine imaginative and prescient and similar subjects.

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L Tv mJ = 0 . Mathematically, it is represented as T mJ E c mJ = 0 This consists of one linear equation and one quadratic equation and gives two solutions, representing the two intersections. In general, they should be conjugate with each other. 5 Algorithm Summarily, the implementation procedure for camera calibration can be divided into the following six steps: 1. Printing an equilateral dodecagon on a sheet paper to make the planar pattern; 2. Moving either the planar pattern or the camera to take a few (at least three) images under different locations; 3.

Then the indexes for other consecutive planes can be retrieved by simple plus and minus operations. In summary, two different models for the DLP projector are established. The first model is used in Chap. 4 by treating the system as an active stereoscopic vision, while the second model is used in Chap. 5 by treating the system as a collection of light planes and a camera. 1 Pattern Coding Strategy Introduction Studies on the active vision techniques for computation of range data can be traced back to the early 1970s from the work of Shirai and his collaborators [138, 139].

1 .. 2u2 v2 .. 2u2 .. 2v2 .. 5) ⎤ ⎥ ⎥ ⎥ ⎥, ⎥ ⎦ x = (a b c d e f )T mi = (ui , vi ) . T The above equation can be solved by SVD or eigenvalue decomposition to obtain the ellipse vector x, and so is the ellipse matrix E c . 4 39 Solving the Projection of Circular Point Once the vanishing line l v and the ellipse E c have been estimated, the projection of circular point can be solved by their intersections. l Tv mJ = 0 . Mathematically, it is represented as T mJ E c mJ = 0 This consists of one linear equation and one quadratic equation and gives two solutions, representing the two intersections.

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