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9780262062206

The Geometry of Multiple Images: The Laws That Govern the Formation of Multiple Images of a Scene Andsome of Their Applications

by
  • ISBN13:

    9780262062206

  • ISBN10:

    0262062208

  • Format: Hardcover
  • Copyright: 2001-03-01
  • Publisher: Mit Pr
  • Purchase Benefits
List Price: $80.00
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Summary

Over the last forty years, researchers have made great strides in elucidating the laws of image formation, processing, and understanding by animals, humans, and machines. This book describes the state of knowledge in one subarea of vision, the geometric laws that relate different views of a scene. Geometry, one of the oldest branches of mathematics, is the natural language for describing three-dimensional shapes and spatial relations. Projective geometry, the geometry that best models image formation, provides a unified framework for thinking about many geometric problems relevant to vision. The book formalizes and analyzes the relations between multiple views of a scene from the perspective of various types of geometries. A key feature is that it considers Euclidean and affine geometries as special cases of projective geometry. Images play a prominent role in computer communications. Producers and users of images, in particular three-dimensional images, require a framework for stating and solving problems. The book offers a number of conceptual tools and theoretical results useful for the design of machine vision algorithms. It also illustrates these tools and results with many examples of real applications.

Table of Contents

Preface xiii
Notation xix
A tour into multiple image geometry
1(62)
Multiple image geometry and three-dimensional vision
2(2)
Projective geometry
4(7)
2-D and 3-D
11(3)
Calibrated and uncalibrated capabilities
14(2)
The plane-to-image homography as a projective transformation
16(2)
Affine description of the projection
18(1)
Structure and motion
19(2)
The homography between two images of a plane
21(1)
Stationary cameras
22(1)
The epipolar constraint between corresponding points
23(1)
The Fundamental matrix
24(2)
Computing the Fundamental matrix
26(2)
Planar homographies and the Fundamental matrix
28(3)
A stratified approach to reconstruction
31(1)
Projective reconstruction
31(4)
Reconstruction is not always necessary
35(1)
Affine reconstruction
36(3)
Euclidean reconstruction
39(5)
The geometry of three images
44(1)
The Trifocal tensor
45(4)
Computing the Trifocal tensor
49(2)
Reconstruction from N images
51(3)
Self-calibration of a moving camera using the absolute conic
54(2)
From affine to Euclidean
56(1)
From projective to Euclidean
57(2)
References and further reading
59(4)
Projective, affine and Euclidean geometries
63(64)
Motivations for the approach and overview
65(4)
Projective spaces: basic definitions
66(1)
Projective geometry
67(1)
Affine geometry
68(1)
Euclidean geometry
69(1)
Affine spaces and affine geometry
69(4)
Definition of an affine space and an affine basis
69(1)
Affine morphisms, affine group
70(2)
Change of affine basis
72(1)
Affine subspaces, parallelism
73(1)
Euclidean spaces and Euclidean geometry
73(5)
Euclidean spaces, rigid displacements, similarities
74(1)
The isotropic cone
75(3)
Projective spaces and projective geometry
78(15)
Basic definitions
78(1)
Projective bases, projective morphisms, homographies
79(9)
Projective subspaces
88(5)
Affine and projective geometry
93(8)
Projective completion of an affine space
93(2)
Affine and projective bases
95(3)
Affine subspace Xn of a projective space Pn
98(1)
Relation between PLG(X) and AG(X)
99(2)
More projective geometry
101(15)
Cross-ratios
101(5)
Duality
106(6)
Conics, quadrics and their duals
112(4)
Projective, affine and Euclidean geometry
116(6)
Relation between PLG(X) and S(X)
117(2)
Angles as cross-ratios
119(3)
Summary
122(2)
References and further reading
124(3)
Exterior and double or Grassmann-Cayley algebras
127(46)
Definition of the exterior algebras of the join
129(8)
First definitions: The join operator
129(4)
Properties of the join operator
133(4)
Plucker relations
137(3)
Derivation of the Plucker relations
137(2)
The example of 3D lines: II
139(1)
The example of 3D planes: II
140(1)
The meet operator: The Grassmann-Cayley algebra
140(6)
Definition of the meet
140(4)
Some planar examples
144(1)
Some 3D examples
145(1)
Duality and the Hodge operator
146(22)
Duality
147(6)
The example of 3D lines: III
153(2)
The Hodge operator
155(3)
The example of 2D lines: II
158(4)
The example of 3D planes: III
162(2)
The example of 3D lines: IV
164(4)
Summary and conclusion
168(1)
References and further reading
169(4)
One camera
173(74)
The Projective model
175(21)
The pinhole camera
176(4)
The projection matrix
180(5)
The inverse projection matrix
185(6)
Viewing a plane in space: The single view homography
191(3)
Projection of a line
194(2)
The affine model: The case of perspective projection
196(11)
The projection matrix
197(3)
The inverse perspective projection matrix
200(2)
Vanishing points and lines
202(5)
The Euclidean model: The case of perspective projection
207(9)
Intrinsic and Extrinsic parameters
207(4)
The absolute conic and the intrinsic parameters
211(5)
The affine and Euclidean models: The case of parallel projection
216(16)
Orthographic, weak perspective, para-perspective projections
216(6)
The general model: The affine projection matrix
222(5)
Euclidean interpretation of the parallel projection
227(5)
Departures from the pinhole model: Nonlinear distortion
232(4)
Nonlinear distortion of the pinhole model
232(2)
Distortion correction within a projective model
234(2)
Calibration techniques
236(4)
Coordinates-based methods
237(2)
Using single view homographies
239(1)
Summary and discussion
240(2)
References and further reading
242(5)
Two views: The Fundamental matrix
247(68)
Configurations with no parallax
250(8)
The correspondence between the two images of a plane
251(4)
Identical optical centers: Application to mosaicing
255(3)
The Fundamental matrix
258(20)
Geometry: The epipolar constraint
259(5)
Algebra: The bilinear constraint
264(2)
The epipolar homography
266(4)
Relations between the Fundamental matrix and planar homographies
270(5)
The S-matrix and the intrinsic planes
275(3)
Perspective projection
278(14)
The affine case
278(2)
The Euclidean case: Epipolar geometry
280(2)
The Essential matrix
282(4)
Structure and motion parameters for a plane
286(1)
Some particular cases
287(5)
Parallel projection
292(8)
Affine epipolar geometry
292(2)
Cyclopean and affine viewing
294(3)
The Euclidean case
297(3)
Ambiguity and the critical surface
300(9)
The critical surfaces
301(2)
The quadratic transformation between two ambiguous images
303(4)
The planar case
307(2)
Summary
309(1)
References and further reading
310(5)
Estimating the Fundamental matrix
315(44)
Linear methods
317(4)
An important normalization procedure
317(1)
The basic algorithm
318(2)
Enforcing the rank constraint by approximation
320(1)
Enforcing the rank constraint by parameterization
321(4)
Parameterizing by the epipolar homography
322(2)
Computing the Jacobian of the parameterization
324(1)
Choosing the best map
325(1)
The distance minimization approach
325(4)
The distance to epipolar lines
326(1)
The Gradient criterion and an interpretation as a distance
327(1)
The ``optimal'' method
328(1)
Robust Methods
329(6)
M-Estimators
330(2)
Monte-Carlo methods
332(3)
An example of Fundamental matrix estimation with comparison
335(6)
Computing the uncertainty of the Fundamental matrix
341(5)
The case of an explicit function
341(1)
The case of an implicit function
342(1)
The error function is a sum of squares
343(2)
The hyper-ellipsoid of uncertainty
345(1)
The case of the Fundamental matrix
345(1)
Some applications of the computation of ΛF
346(7)
Uncertainty of the epipoles
346(3)
Epipolar Band
349(4)
References and further reading
353(6)
Stratification of binocular stereo and applications
359(50)
Canonical representations of two views
361(1)
Projective stratum
362(18)
The projection matrices
362(3)
Projective reconstruction
365(2)
Dealing with real correspondences
367(1)
Planar parallax
368(4)
Image rectification
372(4)
Application to obstacle detection
376(2)
Application to image based rendering from two views
378(2)
Affine stratum
380(13)
The projection matrices
381(4)
Affine reconstruction
385(1)
Affine parallax
386(1)
Estimating H∞
387(4)
Application to affine measurements
391(2)
Euclidean stratum
393(10)
The projection matrices
393(2)
Euclidean reconstruction
395(1)
Euclidean parallax
396(1)
Recovery of the intrinsic parameters
397(4)
Using knowledge about the world: Point coordinates
401(2)
Summary
403(2)
References and further reading
405(4)
Three views: The trifocal geometry
409(60)
The geometry of three views from the viewpoint of two
411(8)
Transfer
412(4)
Trifocal geometry
416(3)
Optical centers aligned
419(1)
The Trifocal tensors
419(20)
Geometric derivation of the Trifocal tensors
419(6)
The six intrinsic planar morphisms
425(3)
Changing the reference view
428(1)
Properties of the Trifocal matrices Gni
429(6)
Relation with planar homographies
435(4)
Prediction revisited
439(2)
Prediction in the Trifocal plane
439(1)
Optical centers aligned
440(1)
Constraints satisfied by the tensors
441(5)
Rank and epipolar constraints
442(1)
The 27 axes constraints
442(3)
The extended rank constraints
445(1)
Constraints that characterize the Trifocal tensor
446(8)
The Affine case
454(1)
The Euclidean case
454(5)
Computing the directions of the translation vectors and the rotation matrices
454(4)
Computing the ratio of the norms of the translation vectors
458(1)
Affine cameras
459(2)
Projective setting
459(1)
Euclidean setting
460(1)
Summary and Conclusion
461(3)
Perspective projection matrices, Fundamental matrices and Trifocal tensors
462(1)
Transfer
463(1)
References and further reading
464(5)
Determining the Trifocal tensor
469(32)
The linear algorithm
471(12)
Normalization again!
471(1)
The basic algorithm
472(4)
Discussion
476(1)
Some results
477(6)
Parameterizing the Trifocal tensor
483(8)
The parameterization by projection matrices
485(1)
The six-point parameterization
486(2)
The tensorial parameterization
488(1)
The minimal one-to-one parameterization
489(2)
Imposing the constraints
491(4)
Projecting by parameterizing
492(1)
Projecting using the algebraic constraints
492(1)
Some results
493(2)
A note about the ``change of view'' operation
495(1)
Nonlinear methods
495(5)
The nonlinear scheme
496(1)
A note about the geometric criterion
497(2)
Results
499(1)
References and further reading
500(1)
Stratification of n ≥ 3 views and applications
501(38)
Canonical representations of n views
503(1)
Projective stratum
503(22)
Beyond the Fundamental matrix and the Trifocal tensor
504(2)
The projection matrices: Three views
506(6)
The projection matrices: An arbitrary number of views
512(13)
Affine and Euclidean strata
525(4)
Stereo rigs
529(7)
Affine calibration
529(4)
Euclidean calibration
533(3)
References and further reading
536(3)
Self-calibration of a moving camera: From affine or projective calibration to full Euclidean calibration
539(54)
From affine to Euclidean
542(7)
Theoretical analysis
542(4)
Practical computation
546(1)
A numerical example
546(2)
Application to panoramic mosaicing
548(1)
From projective to Euclidean
549(11)
The rigidity constraints: Algebraic formulations using the Essential matrix
550(3)
The Kruppa equations: A geometric interpretation of the rigidity constraint
553(4)
Using two rigid displacements of a camera: A method for self-calibration
557(3)
Computing the intrinsic parameters using the Kruppa equations
560(5)
Recovering the focal lengths for two views
560(2)
Solving the Kruppa equations for three views
562(1)
Nonlinear optimization to accumulate the Kruppa equations for n > 3 views: The ``Kruppa'' method
563(2)
Computing the Euclidean canonical form
565(7)
The affine camera case
565(2)
The general formulation in the perspective case
567(5)
Computing all the Euclidean parameters
572(10)
Simultaneous computation of motion and intrinsic parameters: The ``Epipolar/Motion'' method
573(4)
Global optimization on structure, motion, and calibration parameters
577(1)
More applications
578(4)
Degeneracies in self-calibration
582(6)
The spurious absolute conics lie in the real plane at infinity
583(4)
Degeneracies of the Kruppa equations
587(1)
Discussion
588(1)
References and further reading
589(4)
A Appendix 593(4)
A.1 Solution of minx||Ax||2 subject to ||x||2 = 1
593(1)
A.2 A note about rank-2 matrices
594(3)
References 597(38)
Index 635

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