Automatic Calibration and Reconstruction for Active Vision by Beiwei Zhang

By Beiwei Zhang

In this publication, the layout of 2 new planar styles for digicam calibration of intrinsic parameters is addressed and a line-based technique for distortion correction is advised. The dynamic calibration of based mild structures, which encompass a digicam and a projector is usually handled. additionally, the 3D Euclidean reconstruction by utilizing 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 tools, analytic options are supplied for the computational potency and redundancy within the facts should be simply integrated to enhance reliability of the estimations. This quantity will accordingly end up worthwhile and functional instrument for researchers and practioners operating in snapshot processing and machine imaginative and prescient and similar subjects.

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Chapter 3 Static Calibration The camera calibration is an important step towards structure from motion, automobile navigation and many other vision tasks. g. the focal lengths, the skew factor and the principal point. In practice, a good calibration algorithm should be robust, computational efficient and as convenient as possible for implementing. In this chapter, we first introduce the basic theory and methodology for determining camera intrinsic parameters. Then three different planar patterns are designed for camera calibration based on the theories.

10 represent different positions of the vision system Fig. 15 Results of the 3D reconstruction. a and b show the two images together with extracted feature points plotted by RED ‘ + ’. c gives a close look at the fifth image. 15d provides all the reconstructed 3D points. It is noted that the 4th, 5th, 25th and 26th neighboring points are connected to obtain four line segments, which represent the inner edges of the computer screen. We can see that the relative positions of those points accord with the real scene.

Once the image of the absolute conic Θ is available, the camera intrinsic parameters can be recovered by Cholesky decomposition. 18) and stacking them all into a linear system. Step 6: Solving the linear system by SVD to obtain Θ and then recovering the camera matrix K. 1 Numerical Simulations In the numerical simulations, we will test the accuracy and robustness of the suggested technique against different levels of Gaussian noise. Here, the camera matrix is assumed to be K = [1000, 2, 200; 0, 900, 100; 0, 0, 1].

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