By Beiwei Zhang
In this booklet, the layout of 2 new planar styles for digicam calibration of intrinsic parameters is addressed and a line-based approach for distortion correction is advised. The dynamic calibration of based mild platforms, which include a digital camera and a projector is additionally 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 basic matrix are largely studied. In those equipment, analytic recommendations are supplied for the computational potency and redundancy within the facts may be simply integrated to enhance reliability of the estimations. This quantity will accordingly end up precious and functional instrument for researchers and practioners operating in photograph processing and machine imaginative and prescient and comparable subjects.
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Extra info for Automatic Calibration and Reconstruction for Active Vision Systems
There are three eigenvalues obtained since the matrix size is 3 × 3. 1, two of them are identical and different from the third one. The correct solution for the center projection is the eigenvector corresponding to the third eigenvalue. 13). 46 3 Static Calibration Fig. 3 mi Vanishing Line Orthogonality-Based Algorithm Once the vanishing line has been recovered, the intersection of this line with one of the two ellipses gives the image of the circular point. Hence, similar procedure as in Sect.
E. 2) K c = ⎣ 0 fv 0⎦ 0 0 1 In general, the camera coordinate system does not coincide with the world coordinate system and their relationship can be described by a rotation matrix and a translation vector, denoted by R c and tc respectively. Let M be a 3D point in the world coordinate ˜ its corresponding homogeneous representation. 3) where β is a nonzero scale factor. e. a projection device relating the 3D world and the 2D image. So the projector image formation can be approximated by a pin-hole projection model, similar to the pin-hole camera model.
These characteristics greatly simplify the pattern making and the image processing. So it satisfies the two standards. 40 3 Static Calibration Fig. 1 A pair of Circular Points Intersectant-Circle-based Calibration Planar Pattern Design In the previous section, the key problem is how to efficiently find the image of the circular points, which represents the intersection of the vanishing line and the ellipse. Here, we have two important observations: one is that the circular point has nothing to do with the position and metric size of the circle in the planar pattern; and the other is that the intersection of two circles is an invariant property under projective transformation.
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