High Precision Camera Calibration [electronic resource] /
Hanning, Tobias.author.
A calibrated camera is the key to each optical measurement problem. This work introduces camera modelling and calibration in computer vision. Tobias Hanning explains the classic pinhole camera model with distortion derived from third order optics and shows how to obtain an optimal solution within this model. The limitations of the classic camera model in computer vision are explained and proven experimentally. To overcome these limitations two alternative approaches which augment the standard model are presented.
Description based upon print version of record.
Contents; Abstract; Symbols; Chapter 1 Introduction; 1.1 Motivation; 1.2 Outline; 1.3 Contribution; Chapter 2 Modelling the camera mapping; 2.1 Geometric optics for computer vision; 2.1.1 The "thin lens" assumption and first order optics; 2.1.2 The circle of confusion; 2.1.3 Image acquisition; 2.1.3.1 The sensor array; 2.1.3.2 A simplified sensor model; 2.1.3.3 The sensor array as coordinate system; 2.2 The pinhole camera model; Definition 2.2.1; Definition 2.2.2 (Pinhole camera); 2.3 Third order optics and thick lenses; Remark 2.3.1 (Chromatic aberrations)
2.4 The pinhole camera model with distortion2.4.1 Definition; Definition 2.4.1 (Distortion model); Remark 2.4.2; Definition 2.4.3 (Pinhole camera with distortion); 2.4.2 Radial distortion; Definition 2.4.4 (Radial distortion); 2.4.3 Radius transformations; 2.4.4 Other distortion functions; 2.4.4.1 Misaligned thin lens; 2.4.4.2 Misaligned lens systems; 2.5 Inverting the camera mapping; Definition 2.5.1 (undistortion, re-projection); Remark 2.5.2; 2.6 The pinhole camera model in homogeneous co-ordinates; Remark 2.6.1; Remark 2.6.2; Lemma 2.6.3
Chapter 3 Error functions for camera calibration and 3D reconstruction3.1 Introduction; 3.2 Projective and re-projective error; Definition 3.2.1 (Projective Error); Definition 3.2.2 (Re-projective Error); 3.3 Euclidean error; Definition 3.3.1 (Euclidean error); Definition 3.3.2 (Normalized Euclidean error); Remark 3.3.3; Remark 3.3.4 (In front of and behind the camera); 3.4 Error functions for camera calibration and 3D-reconstruction; 3.4.1 Calibration error functions; Remark 3.4.1 (Root mean square error); Remark 3.4.2 (Existence of an optimal solution); Remark 3.4.3 (Complete Calibration)
3.4.2 Reconstruction error functionsRemark 3.4.4; 3.5 Non-linear optimization; Chapter 4 Initial values for camera calibration problems; 4.1 Introduction; 4.2 The two stage method of Tsai; 4.3 An initial image transformation by direct linear transformation; Remark 4.3.1 (negative values for; Remark 4.3.2; 4.4 An initial image transformation from homogra-phies; 4.4.1 Introduction; 4.4.2 Two necessary conditions for planar targets; Remark 4.4.1; 4.4.3 Zhang's initial value; Remark 4.4.2 (Minimal number of observations); 4.4.4 An initial image transformation with known center and zero skew
Remark 4.4.3 (4.4.5 An initial image transformation with known aspect ratio and no skew; 4.4.6 An initial image transformation with known aspect ratio and unknown skew; Remark 4.4.4; 4.4.7 An initial image transformation with no skew; 4.4.7.1 A straight forward constraint; 4.4.7.2 A solution by a linear least squares problem with Cholesky decom-position; 4.4.8 Experimental results; 4.4.8.1 Overview; 4.4.8.2 Simulations; 4.5 An initial value for the extrinsic camera param-eters; 4.5.1 Introduction and problem statement; 4.5.2 Standard pose estimation
4.5.3 An algebraic re-projective approach for regular grids
Includes bibliographical references and index.
A calibrated camera is the key to each optical measurement problem. This work introduces camera modelling and calibration in computer vision. Tobias Hanning explains the classic pinhole camera model with distortion derived from third order optics and shows how to obtain an optimal solution within this model. The limitations of the classic camera model in computer vision are explained and proven experimentally. To overcome these limitations two alternative approaches which augment the standard model are presented.
Optical data processing.
Computer science.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer Science, general.