LEADER 05397nam 2200673Ia 450 001 9910830635703321 005 20230721005741.0 010 $a1-282-16494-5 010 $a9786612164941 010 $a0-470-61104-9 010 $a0-470-39362-9 035 $a(CKB)2550000000006379 035 $a(EBL)479822 035 $a(OCoLC)520990428 035 $a(SSID)ssj0000344260 035 $a(PQKBManifestationID)11260135 035 $a(PQKBTitleCode)TC0000344260 035 $a(PQKBWorkID)10306952 035 $a(PQKB)11719344 035 $a(MiAaPQ)EBC479822 035 $a(EXLCZ)992550000000006379 100 $a20070711d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aVisual perception through video imagery$b[electronic resource] /$fedited by Michel Dhome 210 $aLondon $cISTE ;$aHoboken, N.J. $cWiley$d2009 215 $a1 online resource (309 p.) 225 1 $aISTE ;$vv.19 300 $a"First published in France in 2001 by Herme?s Science/Lavoiser entitled 'Perception visuelle par imagerie video'" --T.p. verso. 311 $a1-84821-016-7 320 $aIncludes bibliographical references and index. 327 $aVisual Perception through Video Imagery; Table of Contents; Introduction; Part 1; Chapter 1. Calibration of Vision Sensors; 1.1. Introduction; 1.2. General formulation of the problem of calibration; 1.2.1. Formulation of the problem; 1.2.1.1. Modeling the camera and lens: pin-hole model; 1.2.1.2. Formation of images: perspective projection; 1.2.1.3. Changing lens/camera reference point; 1.2.1.4. Changing of the camera/image point; 1.2.1.5. Changing of coordinates in the image plane; 1.2.2. General expression; 1.2.2.1. General formulation of the problem of calibration; 1.3. Linear approach 327 $a1.3.1. Principle1.3.2. Notes and comments; 1.4. Non-linear photogrammetric approach; 1.4.1. Mathematic model; 1.4.2. Solving the problem; 1.4.3. Multi-image calibration; 1.4.4. Self-calibration by bundle adjustment; 1.4.4.1. Redefinition of the problem; 1.4.4.2. Estimation of redundancy; 1.4.4.3. Solution for a near scale factor; 1.4.4.4. Initial conditions; 1.4.5. Precision calculation; 1.5. Results of experimentation; 1.5.1. Bundle adjustment for a traditional lens; 1.5.1.1. Initial and experimental conditions; 1.5.1.2. Sequence of classic images; 1.5.2. Specific case of fish-eye lenses 327 $a1.5.2.1. Traditional criterion1.5.2.2. Zero distortion at r0; 1.5.2.3. Normalization of distortion coefficients; 1.5.2.4. Experiments; 1.5.3. Calibration of underwater cameras; 1.5.3.1. Theoretical notes; 1.5.3.2. Experiments; 1.5.3.3. The material; 1.5.3.4. Results in air; 1.5.3.5. Calibration in water; 1.5.3.6. Relation between the calibration in air and in water; 1.5.4. Calibration of zooms; 1.5.4.1. Recalling optical properties; 1.5.4.2. Estimate of the principal point; 1.5.4.3. Experiments; 1.6. Bibliography; Chapter 2. Self-Calibration of Video Sensors; 2.1. Introduction 327 $a2.2. Reminder and notation2.3. Huang-Faugeras constraints and Trivedi's equations; 2.3.1. Huang-Faugeras constraints; 2.3.2. Trivedi's constraints; 2.3.3. Discussion; 2.4. Kruppa equations; 2.4.1. Geometric derivation of Kruppa equations; 2.4.2. An algebraic derivation of Kruppa equations; 2.4.3. Simplified Kruppa equations; 2.5. Implementation; 2.5.1. The choice of initial conditions; 2.5.2. Optimization; 2.6. Experimental results; 2.6.1. Estimation of angles and length ratios from images; 2.6.2. Experiments with synthetic data; 2.6.3. Experiments with real data; 2.7. Conclusion 327 $a2.8. Acknowledgement2.9. Bibliography; Chapter 3. Specific Displacements for Self-calibration; 3.1. Introduction: interest to resort to specific movements; 3.2. Modeling: parametrization of specific models; 3.2.1. Specific projection models; 3.2.2. Specifications of internal parameters of the camera; 3.2.3. Taking into account specific displacements; 3.2.4. Relation with specific properties in the scene; 3.3. Self-calibration of a camera; 3.3.1. Usage of pure rotations or points at the horizon; 3.3.2. Pure rotation and fixed parameters; 3.3.3. Rotation around a fixed axis 327 $a3.4. Perception of depth 330 $aFor several decades researchers have tried to construct perception systems based on the registration data from video cameras. This work has produced various tools that have made recent advances possible in this area. Part 1 of this book deals with the problem of the calibration and auto-calibration of video captures. Part 2 is essentially concerned with the estimation of the relative object/capture position when a priori information is introduced (the CAD model of the object). Finally, Part 3 discusses the inference of density information and the shape recognition in images. 410 0$aISTE 606 $aComputer vision 606 $aVisual perception 606 $aVision 615 0$aComputer vision. 615 0$aVisual perception. 615 0$aVision. 676 $a006.3/7 676 $a006.42 686 $aST 330$2rvk 701 $aDhome$b Michel$01612566 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830635703321 996 $aVisual perception through video imagery$93941439 997 $aUNINA