By Luis Puig, J.J. Guerrero (auth.)
This paintings makes a speciality of critical catadioptric structures, from the early step of calibration to high-level projects comparable to 3D info retrieval. The publication opens with a radical creation to the sector digicam version, besides an research of the relation among this version and genuine primary catadioptric platforms. Then, a brand new method of calibrate any single-viewpoint catadioptric digital camera is defined. this is often by means of an research of latest tools for calibrating important omnivision platforms, and a close exam of hybrid two-view family members that mix pictures bought with uncalibrated valuable catadioptric platforms and standard cameras. within the ultimate chapters, the booklet discusses a brand new strategy to compute the size area of any omnidirectional photograph obtained with a vital catadioptric method, and a method for computing the orientation of a handheld omnidirectional catadioptric camera.
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Extra resources for Omnidirectional Vision Systems: Calibration, Feature Extraction and 3D Information
Compared with Fig. 2b, where the intersection of the three planes of the grid is at the image center. We observe improvement with rotation specially for increased distance-to-grid since grid points are more spread and effect of noise decreases. 1, we list the results of the algorithm after linear (DLT) and nonlinear steps for a few cases. Our main observation is that the errors in linear estimates, ξDLT and f DLT , are biased (values are smaller than they should be). For all the cases, however, the true intrinsic parameters are reached after nonlinear optimization, modulo errors due to noise.
We take as initial values the parameters obtained from Pcata and initialize the additional 4 distortion parameters and the tilt angles in R p , by zero. 2 Algorithm to Compute Pcata Here we summarize the algorithm used to compute the generic projection matrix Pcata . 1. Linear Solution. Using 3D–2D correspondences we compute Pcata by a DLT-like approach. 2. Intrinsic/Extrinsic Parameter Extraction. , there is no tilting and that the images are not distorted. We extract from the linear solution, the intrinsic (ξ, f, cx , c y ) and extrinsic (α, β, γ , tx , t y , tz ) parameters in closed-form.
1 mm. 5 shows the configuration used for the SfM experiment. Using the calibration provided by each method we compute the corresponding 3D 9 PhotoModeler software was used. 4 Experiments 45 Fig. 2 Reprojection error in pixels as a function of noise (σ ) corresponding to the hypercatadioptric system Fig. 3 Reprojection error in pixels as a function of noise (σ ) corresponding to the paracatadioptric system rays from each omnidirectional image. The correspondences between the two images were given manually.