LEADER 04500nam 2200721Ia 450 001 9910965469303321 005 20250212212215.0 010 $a9786610458127 010 $a9781107141698 010 $a1107141699 010 $a9781139636124 010 $a113963612X 010 $a9781280458125 010 $a1280458127 010 $a9780511184512 010 $a0511184514 010 $a9780511185359 010 $a0511185359 010 $a9780511187117 010 $a0511187114 010 $a9780511313332 010 $a0511313330 010 $a9780511811685 010 $a0511811683 010 $a9780511186189 010 $a0511186185 035 $a(MiAaPQ)EBC256634 035 $a(Au-PeEL)EBL256634 035 $a(CaPaEBR)ebr10126077 035 $a(CaONFJC)MIL45812 035 $a(OCoLC)935231096 035 $a(PPN)172020824 035 $a(CKB)1000000000354238 035 $a(UkCbUP)CR9780511811685 035 $a(EXLCZ)991000000000354238 100 $a20040922d2003 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultiple view geometry in computer vision /$fRichard Hartley, Andrew Zisserman 205 $a2nd ed. 210 $aCambridge, UK ;$aNew York $cCambridge University Press$d2003 215 $a1 online resource (xvi, 655 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 320 $aIncludes bibliographical references (p. 634-645) and index. 327 $a1. Introduction - a tour of multiple view geometry -- Part 0. The Background: Projective Geometry, Transformations and Estimation -- 2. Projective geometry and transformations of 2D -- 3. Projective geometry and transformations of 3D -- 4. Estimation - 2D projective transforms -- 5. Algorithm evaluation and error analysis -- Part I. Camera Geometry and Single View Geometry -- 6. Camera models -- 7. Computation of the camera matrix -- 8. More single view geometry -- Part II. Two-View Geometry -- 9. Epipolar geometry and the fundamental matrix -- 10. 3D reconstruction of cameras and structure -- 11. Computation of the fundamental matrix F -- 12. Structure computation -- 13. Scene planes and homographies -- 14. Affine epipolar geometry -- Part III. Three-View Geometry -- 15. The trifocal tensor -- 16. Computation of the trifocal tensor T -- Part IV. N -View Geometry -- 17. N-linearities and multiple view tensors -- 18. N-view computational methods -- 19. Auto-calibration -- 20. Duality -- 21. Chirality -- 22. Degenerate configurations -- Part V. Appendices -- Appendix 1. Tensor notation -- Appendix 2. Gaussian (normal) and chi-squared distributions -- Appendix 3. Parameter estimation -- Appendix 4. Matrix properties and decompositions -- Appendix 5. Least-squares minimization -- Appendix 6. Iterative Estimation Methods -- Appendix 7. Some special plane projective transformations -- Bibliography -- Index. 330 $aA basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book. 606 $aComputer vision 606 $aGeometry, Projective 615 0$aComputer vision. 615 0$aGeometry, Projective. 676 $a006.3/7 700 $aHartley$b Richard$0319160 701 $aZisserman$b Andrew$066337 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910965469303321 996 $aMultiple view geometry in computer vision$9783037 997 $aUNINA