Visual perception through video imagery:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English French |
| Veröffentlicht: |
London
ISTE [u.a.]
2009
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Includes index |
| Beschreibung: | 307 S. Ill., graph. Darst. |
| ISBN: | 9781848210165 1848210167 |
Internformat
MARC
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| 020 | |a 1848210167 |9 1-84821-016-7 | ||
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| 130 | 0 | |a Perception visuelle par imagerie video | |
| 245 | 1 | 0 | |a Visual perception through video imagery |c edited by Michel Dhome |
| 264 | 1 | |a London |b ISTE [u.a.] |c 2009 | |
| 300 | |a 307 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes index | ||
| 650 | 4 | |a Computer vision | |
| 650 | 4 | |a Visual perception | |
| 650 | 4 | |a Vision | |
| 650 | 4 | |a Computer vision | |
| 650 | 4 | |a Vision | |
| 650 | 4 | |a Visual perception | |
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| 689 | 0 | 1 | |a Videobild |0 (DE-588)4563329-0 |D s |
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| 700 | 1 | |a Dhome, Michel |e Sonstige |4 oth | |
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| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016956837&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016956837 | ||
Datensatz im Suchindex
| _version_ | 1804138307017244673 |
|---|---|
| adam_text | Table
of
Contents
Introduction
..................................... 13
Part
1 ......................................... 17
Chapter
1.
Calibration of
Vision
Sensors
.................. 19
Jean-Marc LAVEST and Gerard RIVES
1.1.
Introduction
................................. 19
1.2.
General formulation of the problem of calibration
......... 20
1.2.1.
Formulation of the problem
.................... 20
1.2.1.1.
Modeling the camera and lens: pin-hole model
..... 22
1.2.1.2.
Formation of images: perspective projection
...... 22
1.2.1.3.
Changing lens/camera reference point
.......... 23
1.2.1.4.
Changing of the camera/image point
........... 24
1.2.1.5.
Changing of coordinates in the image plane
....... 24
1.2.2.
General expression
......................... 25
1.2.2.1.
General formulation of the problem of calibration
... 27
1.3.
Linear approach
.............................. 27
1.3.1.
Principle
................................ 27
1.3.2.
Notes and comments
........................ 29
1.4.
Non-linear photogrammetric approach
................ 30
1.4.1.
Mathematic model
.......................... 31
1.4.2.
Solving the problem
......................... 34
1.4.3.
Multi-image calibration
...................... 35
1.4.4.
Self-calibration by bundle adjustment
.............. 36
1.4.4.1.
Redefinition of the problem
................. 36
1.4.4.2.
Estimation of redundancy
.................. 37
1.4.4.3.
Solution for a near scale factor
............... 37
1.4.4.4.
Initial conditions
....................... 38
6
Visual
Perception
through Video Imagery
1.4.5.
Precision calculation
........................ 38
1.5.
Results of experimentation
........................ 39
1.5.1.
Bundle adjustment for a traditional lens
............ 39
1.5.1.1.
Initial and experimental conditions
............ 39
1.5.1.2.
Sequence of classic images
................. 40
1.5.2.
Specific case of fish-eye lenses
.................. 42
1.5.2.1.
Traditional criterion
..................... 43
1.5.2.2.
Zero distortion at ro
..................... 43
1.5.2.3.
Normalization of distortion coefficients
......... 44
1.5.2.4.
Experiments
.......................... 45
1.5.3.
Calibration of underwater cameras
................ 48
1.5.3.1.
Theoretical notes
....................... 48
1.5.3.2.
Experiments
.......................... 49
1.5.3.3.
The material
.......................... 49
1.5.3.4.
Results in air
.......................... 49
1.5.3.5.
Calibration in water
...................... 50
1.5.3.6.
Relation between the calibration in air and in water
. . 53
1.5.4.
Calibration of zooms
........................ 55
1.5.4.1.
Recalling optical properties
................. 55
1.5.4.2.
Estimate of the principal point
............... 56
1.5.4.3.
Experiments
.......................... 57
1.6.
Bibliography
................................ 58
Chapter
2.
Self-Calibration of Video Sensors
............... 61
Rachid DERICHE
2.1.
Introduction
................................. 61
2.2.
Reminder and notation
.......................... 64
2.3.
Huang-Faugeras constraints and Trivedi s equations
........ 66
2.3.1.
Huang-Faugeras constraints
.................... 66
2.3.2.
Trivedi s constraints
......................... 67
2.3.3.
Discussion
.............................. 68
2.4.
Kruppa
equations
............................. 68
2.4.1.
Geometric derivation of
Kruppa
equations
........... 68
2.4.2.
An algebraic derivation of
Kruppa
equations
......... 70
2.4.3.
Simplified
Kruppa
equations
................... 72
2.5.
Implementation
............................... 74
2.5.1.
The choice of initial conditions
.................. 74
2.5.2.
Optimization
............................. 75
2.6.
Experimental results
............................ 76
2.6.1.
Estimation of angles and length ratios from images
....
77
Table
of
Contents
7
2.6.2.
Experiments with synthetic data
................. 78
2.6.3.
Experiments with real data
.................... 79
2.7.
Conclusion
................................. 85
2.8.
Acknowledgement
............................. 87
2.9.
Bibliography
................................ 87
Chapter
3.
Specific Displacements for Self-calibration
......... 91
Diane LINGRAND,
François GASPARD
and Thierry
VIÉVILLE
3.1.
Introduction: interest to resort to specific movements
....... 91
3.2.
Modeling: parametrization of specific models
............ 93
3.2.1.
Specific projection models
..................... 93
3.2.2.
Specifications of internal parameters of the camera
..... 96
3.2.3.
Taking into account specific displacements
.......... 97
3.2.4.
Relation with specific properties in the scene
......... 100
3.3.
Self-calibration of a camera
....................... 100
3.3.1.
Usage of pure rotations or points at the horizon
........ 103
3.3.2.
Pure rotation and fixed parameters
................ 104
3.3.3.
Rotation around a fixed axis
.................... 106
3.4.
Perception of depth
............................ 108
3.4.1.
Usage of pure translations
..................... 108
3.4.2.
Retinal movements
.........................
Ill
3.4.3.
Variation of the focal length
.................... 114
3.5.
Estimating a specific model on real data
............... 119
3.5.1.
Application of the estimation mechanism to model inference
122
3.5.2.
Some experimental results
..................... 123
3.5.3.
Application at the localization of
aplane
............ 125
3.5.3.1.
Rotation in pitch and calibration from a plane
..... 130
3.6.
Conclusion
................................. 136
3.7.
Bibliography
................................ 136
Part
2......................................... 143
Chapter
4.
Localization Tools
......................... 145
Michel DHOME and Jean-Thierry
LAPRESTÉ
4.1.
Introduction
................................. 145
4.2.
Geometric modeling of a video camera
................ 146
4.2.1.
Pinhole model
............................ 146
4.2.2.
Perspective projection of a
3D
point
............... 147
4.3.
Localization of a voluminous object by monocular vision
.... 148
4.3.1.
Introduction
.............................. 148
4.3.2.
Mappings
............................... 149
8 Visual
Perception
through Video Imagery
4.3.2.1.
Matching of lines
....................... 149
4.3.2.2.
Pairing of points
........................ 150
4.3.3.
Criterion to minimize
........................ 152
4.3.4.
Solving the problem using the Newton-Raphson method
. . 153
4.3.5.
Calculation of partial derivatives
................. 154
4.3.6.
Results
................................. 156
4.4.
Localization of a voluminous object by multi-ocular vision
... 158
4.4.1.
Mathematical developments
.................... 158
4.4.2.
Calculation of partial derivatives
................. 159
4.4.3.
Results
................................. 159
4.5.
Localization of an articulated object
.................. 161
4.5.1.
Mathematical development
.................... 161
4.5.2.
Calculation of partial derivatives for intrinsic parameters
. . 163
4.5.3.
Results
................................. 163
4.6.
Hand-eye calibration
........................... 164
4.6.1.
Introduction
.............................. 164
4.6.2.
Presentation of the method
.................... 164
4.6.3.
Geometric constraint
........................ 166
4.6.4.
Results
................................. 166
4.7.
Initialization methods
........................... 168
4.7.1.
Initial hypotheses
.......................... 168
4.7.2.
Objective
............................... 169
4.7.3.
Under the hypothesis of perspective projection
........ 170
4.7.4.
Under the hypothesis of scaled orthographic projection
. . . 172
4.7.5.
Development of the algorithm
.................. 173
4.7.6.
Specific case of a planar object
.................. 174
4.8.
Analytical calculations of localization errors
............. 177
4.8.1.
Uncertainties in the estimation of a line equation
....... 177
4.8.2.
Errors in normals
.......................... 179
4.8.3.
Uncertainties in final localization of polyhedral objects
. . . 181
4.8.3.1.
Covariance matrix associated with the localization
parameters
............................ 181
4.9.
Conclusion
................................. 183
4.10.
Bibliography
................................ 183
Part3
......................................... 187
Chapter
5.
Reconstruction of
3D
Scenes from Multiple Views
.... 189
Long QUAN, Luce MORIN and Lionel OISEL
5.1.
Introduction
................................. 189
5.2.
Geometry relating to the acquisition of multiple images
...... 189
Table
of
Contents
9
5.2.1.
Geometry of two
images
...................... 189
5.2.1.1.
Geometric aspect
....................... 190
5.2.1.2.
Algebraic aspect
........................ 191
5.2.1.3.
Properties of
F
......................... 191
5.2.1.4.
Estimation of the fundamental matrix
.......... 192
5.2.1.5. 7
point algorithm
....................... 192
5.2.1.6. 8
point algorithm
....................... 193
5.2.1.7.
Optimal algorithms
...................... 193
5.2.1.8.
Robust algorithms which make it possible to eliminate
false pairing between a couple of points
.......... 194
5.2.2.
Geometry of
3
images
....................... 195
5.2.3.
Geometry beyond
3
images
.................... 199
5.3.
Matching
................................... 200
5.3.1.
State of the art elements
...................... 200
5.3.1.1.
Correlation
........................... 201
5.3.1.2.
Block-matching
........................ 202
5.3.1.3.
Dynamic programming
................... 202
5.3.1.4.
Association of the optical flow and
epipolar
geometry
202
5.3.1.5.
Energy modeling
....................... 204
5.3.2.
Dense estimation algorithm based on optical flow
...... 205
5.3.2.1.
Hypothesis for the conservation of brightness
..... 205
5.3.2.2.
Energy modeling
....................... 206
5.3.2.3.
Multi-resolution minimization diagram
......... 207
5.4. 3D
reconstruction
............................. 208
5.4.1.
Reconstruction principle: retro-projection
........... 209
5.4.2.
Projective reconstruction
...................... 209
5.4.3.
Euclidean reconstruction
...................... 212
5.4.3.1.
Calibrated cameras
...................... 212
5.4.3.2.
Known intrinsic parameters
................. 212
5.4.3.3.
Known metric data in the scene
.............. 213
5.5. 3D
modeling
................................ 214
5.5.1.
Implicit model
............................ 214
5.5.2.
Point sets
............................... 216
5.5.3.
Triangular mesh
........................... 216
5.5.3.1.
Interactive designation of mesh vertices
......... 217
5.5.3.2.
Microfacets
........................... 217
5.5.3.3. Triangulation
of the points of interest
........... 217
5.5.3.4.
Adaptive
triangulation
.................... 217
5.5.3.5.
Regular
triangulation
..................... 219
5.6.
Examples of applications
......................... 219
10
Visual
Perception
through Video Imagery
5.6.1.
Virtual view rendering
....................... 219
5.6.2.
VRML models
............................ 220
5.7.
Conclusion
................................. 220
5.8.
Bibliography
................................ 221
Chapter
6. 3D
Reconstruction by Active Dynamic Vision
....... 225
Éric MARCHAND
and
François CHAUMETTE
6.1.
Introduction: active vision
........................ 225
6.2.
Reconstruction of
3D
primitives
.................... 227
6.2.1.
Reconstruction by dynamic vision: a rapid state of the art
. 227
6.2.2.
General principle
.......................... 230
6.2.3.
Some specific cases
......................... 232
6.2.3.1.
Point
............................... 232
6.2.3.2.
Line
............................... 233
6.2.3.3.
Cylinder
............................. 235
6.2.4. 3D
reconstruction by active vision
................ 235
6.2.4.1. 3D
reconstruction by active vision: state of the art
. . 236
6.2.4.2.
Optimal
3D
reconstruction of a primitive
........ 237
6.2.5.
Generation of camera movements
................ 240
6.3.
Reconstruction of a complete scene
.................. 243
6.3.1.
Automatic positioning of the camera for the observation of
the scene
................................ 243
6.3.2.
Scene reconstruction: general principle
............. 244
6.3.3.
Local focusing strategy
....................... 245
6.3.4.
Completeness of reconstruction: selection of viewpoints
. . 247
6.3.4.1.
Calculation of new viewpoints
............... 247
6.3.4.2.
Optimization
.......................... 250
6.4.
Results
.................................... 250
6.4.1.
Reconstruction of
3D
primitive: case of the cylinder
.... 251
6.4.2.
Perception strategies
........................ 252
6.4.2.1.
Local exploration
....................... 252
6.4.2.2.
Total exploration
....................... 254
6.5.
Conclusion
................................. 257
6.6.
Appendix: calculation of the interaction matrix
........... 258
6.7.
Bibliography
................................ 259
Part
4......................................... 263
Chapter
7.
Shape Recognition in Images
.................. 265
Patrick
GROS
and Cordelia
SCHMID
7.1.
Introduction
................................. 265
Table
of
Contents 11
7.2. State
of the art ...............................
266
7.2.1.
Searching images based on photometric data
......... 266
7.2.2.
Search for images based on geometric data
.......... 267
7.2.3.
Recognition using a
3D
geometric model
........... 268
7.2.4.
Recognition using a set of images
................ 270
7.3.
Principle of local quasi-invariants
................... 270
7.4.
Photometric approach
........................... 272
7.4.1.
Key points
............................... 272
7.4.2.
Differential invariants of gray levels
............... 273
7.4.3.
Comparison of descriptors with Mahalanobis distance
... 275
7.4.4.
Voting algorithm
........................... 276
7.4.5.
Semi-local constraints
....................... 277
7.4.6.
Multi-dimensional indexing
.................... 278
7.4.7.
Experimental results
........................ 279
7.4.8.
Extensions
.............................. 282
7.5.
Geometric approach
............................ 284
7.5.1.
Basic algorithm
........................... 284
7.5.2.
Some results
............................. 285
7.5.2.1.
Pairing results
......................... 285
7.5.2.2.
Results of indexing and recognition
............ 286
7.6.
Indexing of images
............................ 288
7.6.1.
Traditional approaches
....................... 290
7.6.2.
VA-File and the Pyramid-Tree
.................. 291
7.6.3.
Some results
............................. 292
7.6.3.1.
Context of experiments
................... 293
7.6.3.2.
First experiment
........................ 293
7.6.3.3.
Second experiment
...................... 293
7.6.3.4.
Third experiment
....................... 294
7.6.4.
Some prospects
........................... 294
7.7.
Conclusion
................................. 295
7.8.
Bibliography
................................ 296
List of Authors
................................... 301
Index
......................................... 305
For several decades researchers have tried to construct per¬
ception systems based on the registration data from video
cameras. This work has produced various tools that have
made recent advances possible in this area.
The first part of this book deals with the problem of the cali¬
bration and auto-calibration of video captures. It then moves
on to the estimation of the relative object/capture position
when a priori information is introduced (the CAD model of
the object). Finally, the inference of density information and
shape recognition in ¡mages are discussed.
Michel Dhome is Research Director at the
CNRS
and is a
Professor at the University of Clermont-Ferrand, France.
|
| adam_txt |
Table
of
Contents
Introduction
. 13
Part
1 . 17
Chapter
1.
Calibration of
Vision
Sensors
. 19
Jean-Marc LAVEST and Gerard RIVES
1.1.
Introduction
. 19
1.2.
General formulation of the problem of calibration
. 20
1.2.1.
Formulation of the problem
. 20
1.2.1.1.
Modeling the camera and lens: pin-hole model
. 22
1.2.1.2.
Formation of images: perspective projection
. 22
1.2.1.3.
Changing lens/camera reference point
. 23
1.2.1.4.
Changing of the camera/image point
. 24
1.2.1.5.
Changing of coordinates in the image plane
. 24
1.2.2.
General expression
. 25
1.2.2.1.
General formulation of the problem of calibration
. 27
1.3.
Linear approach
. 27
1.3.1.
Principle
. 27
1.3.2.
Notes and comments
. 29
1.4.
Non-linear photogrammetric approach
. 30
1.4.1.
Mathematic model
. 31
1.4.2.
Solving the problem
. 34
1.4.3.
Multi-image calibration
. 35
1.4.4.
Self-calibration by bundle adjustment
. 36
1.4.4.1.
Redefinition of the problem
. 36
1.4.4.2.
Estimation of redundancy
. 37
1.4.4.3.
Solution for a near scale factor
. 37
1.4.4.4.
Initial conditions
. 38
6
Visual
Perception
through Video Imagery
1.4.5.
Precision calculation
. 38
1.5.
Results of experimentation
. 39
1.5.1.
Bundle adjustment for a traditional lens
. 39
1.5.1.1.
Initial and experimental conditions
. 39
1.5.1.2.
Sequence of classic images
. 40
1.5.2.
Specific case of fish-eye lenses
. 42
1.5.2.1.
Traditional criterion
. 43
1.5.2.2.
Zero distortion at ro
. 43
1.5.2.3.
Normalization of distortion coefficients
. 44
1.5.2.4.
Experiments
. 45
1.5.3.
Calibration of underwater cameras
. 48
1.5.3.1.
Theoretical notes
. 48
1.5.3.2.
Experiments
. 49
1.5.3.3.
The material
. 49
1.5.3.4.
Results in air
. 49
1.5.3.5.
Calibration in water
. 50
1.5.3.6.
Relation between the calibration in air and in water
. . 53
1.5.4.
Calibration of zooms
. 55
1.5.4.1.
Recalling optical properties
. 55
1.5.4.2.
Estimate of the principal point
. 56
1.5.4.3.
Experiments
. 57
1.6.
Bibliography
. 58
Chapter
2.
Self-Calibration of Video Sensors
. 61
Rachid DERICHE
2.1.
Introduction
. 61
2.2.
Reminder and notation
. 64
2.3.
Huang-Faugeras constraints and Trivedi's equations
. 66
2.3.1.
Huang-Faugeras constraints
. 66
2.3.2.
Trivedi's constraints
. 67
2.3.3.
Discussion
. 68
2.4.
Kruppa
equations
. 68
2.4.1.
Geometric derivation of
Kruppa
equations
. 68
2.4.2.
An algebraic derivation of
Kruppa
equations
. 70
2.4.3.
Simplified
Kruppa
equations
. 72
2.5.
Implementation
. 74
2.5.1.
The choice of initial conditions
. 74
2.5.2.
Optimization
. 75
2.6.
Experimental results
. 76
2.6.1.
Estimation of angles and length ratios from images
.
77
Table
of
Contents
7
2.6.2.
Experiments with synthetic data
. 78
2.6.3.
Experiments with real data
. 79
2.7.
Conclusion
. 85
2.8.
Acknowledgement
. 87
2.9.
Bibliography
. 87
Chapter
3.
Specific Displacements for Self-calibration
. 91
Diane LINGRAND,
François GASPARD
and Thierry
VIÉVILLE
3.1.
Introduction: interest to resort to specific movements
. 91
3.2.
Modeling: parametrization of specific models
. 93
3.2.1.
Specific projection models
. 93
3.2.2.
Specifications of internal parameters of the camera
. 96
3.2.3.
Taking into account specific displacements
. 97
3.2.4.
Relation with specific properties in the scene
. 100
3.3.
Self-calibration of a camera
. 100
3.3.1.
Usage of pure rotations or points at the horizon
. 103
3.3.2.
Pure rotation and fixed parameters
. 104
3.3.3.
Rotation around a fixed axis
. 106
3.4.
Perception of depth
. 108
3.4.1.
Usage of pure translations
. 108
3.4.2.
Retinal movements
.
Ill
3.4.3.
Variation of the focal length
. 114
3.5.
Estimating a specific model on real data
. 119
3.5.1.
Application of the estimation mechanism to model inference
122
3.5.2.
Some experimental results
. 123
3.5.3.
Application at the localization of
aplane
. 125
3.5.3.1.
Rotation in pitch and calibration from a plane
. 130
3.6.
Conclusion
. 136
3.7.
Bibliography
. 136
Part
2. 143
Chapter
4.
Localization Tools
. 145
Michel DHOME and Jean-Thierry
LAPRESTÉ
4.1.
Introduction
. 145
4.2.
Geometric modeling of a video camera
. 146
4.2.1.
Pinhole model
. 146
4.2.2.
Perspective projection of a
3D
point
. 147
4.3.
Localization of a voluminous object by monocular vision
. 148
4.3.1.
Introduction
. 148
4.3.2.
Mappings
. 149
8 Visual
Perception
through Video Imagery
4.3.2.1.
Matching of lines
. 149
4.3.2.2.
Pairing of points
. 150
4.3.3.
Criterion to minimize
. 152
4.3.4.
Solving the problem using the Newton-Raphson method
. . 153
4.3.5.
Calculation of partial derivatives
. 154
4.3.6.
Results
. 156
4.4.
Localization of a voluminous object by multi-ocular vision
. 158
4.4.1.
Mathematical developments
. 158
4.4.2.
Calculation of partial derivatives
. 159
4.4.3.
Results
. 159
4.5.
Localization of an articulated object
. 161
4.5.1.
Mathematical development
. 161
4.5.2.
Calculation of partial derivatives for intrinsic parameters
. . 163
4.5.3.
Results
. 163
4.6.
Hand-eye calibration
. 164
4.6.1.
Introduction
. 164
4.6.2.
Presentation of the method
. 164
4.6.3.
Geometric constraint
. 166
4.6.4.
Results
. 166
4.7.
Initialization methods
. 168
4.7.1.
Initial hypotheses
. 168
4.7.2.
Objective
. 169
4.7.3.
Under the hypothesis of perspective projection
. 170
4.7.4.
Under the hypothesis of scaled orthographic projection
. . . 172
4.7.5.
Development of the algorithm
. 173
4.7.6.
Specific case of a planar object
. 174
4.8.
Analytical calculations of localization errors
. 177
4.8.1.
Uncertainties in the estimation of a line equation
. 177
4.8.2.
Errors in normals
. 179
4.8.3.
Uncertainties in final localization of polyhedral objects
. . . 181
4.8.3.1.
Covariance matrix associated with the localization
parameters
. 181
4.9.
Conclusion
. 183
4.10.
Bibliography
. 183
Part3
. 187
Chapter
5.
Reconstruction of
3D
Scenes from Multiple Views
. 189
Long QUAN, Luce MORIN and Lionel OISEL
5.1.
Introduction
. 189
5.2.
Geometry relating to the acquisition of multiple images
. 189
Table
of
Contents
9
5.2.1.
Geometry of two
images
. 189
5.2.1.1.
Geometric aspect
. 190
5.2.1.2.
Algebraic aspect
. 191
5.2.1.3.
Properties of
F
. 191
5.2.1.4.
Estimation of the fundamental matrix
. 192
5.2.1.5. 7
point algorithm
. 192
5.2.1.6. 8
point algorithm
. 193
5.2.1.7.
Optimal algorithms
. 193
5.2.1.8.
Robust algorithms which make it possible to eliminate
false pairing between a couple of points
. 194
5.2.2.
Geometry of
3
images
. 195
5.2.3.
Geometry beyond
3
images
. 199
5.3.
Matching
. 200
5.3.1.
State of the art elements
. 200
5.3.1.1.
Correlation
. 201
5.3.1.2.
Block-matching
. 202
5.3.1.3.
Dynamic programming
. 202
5.3.1.4.
Association of the optical flow and
epipolar
geometry
202
5.3.1.5.
Energy modeling
. 204
5.3.2.
Dense estimation algorithm based on optical flow
. 205
5.3.2.1.
Hypothesis for the conservation of brightness
. 205
5.3.2.2.
Energy modeling
. 206
5.3.2.3.
Multi-resolution minimization diagram
. 207
5.4. 3D
reconstruction
. 208
5.4.1.
Reconstruction principle: retro-projection
. 209
5.4.2.
Projective reconstruction
. 209
5.4.3.
Euclidean reconstruction
. 212
5.4.3.1.
Calibrated cameras
. 212
5.4.3.2.
Known intrinsic parameters
. 212
5.4.3.3.
Known metric data in the scene
. 213
5.5. 3D
modeling
. 214
5.5.1.
Implicit model
. 214
5.5.2.
Point sets
. 216
5.5.3.
Triangular mesh
. 216
5.5.3.1.
Interactive designation of mesh vertices
. 217
5.5.3.2.
Microfacets
. 217
5.5.3.3. Triangulation
of the points of interest
. 217
5.5.3.4.
Adaptive
triangulation
. 217
5.5.3.5.
Regular
triangulation
. 219
5.6.
Examples of applications
. 219
10
Visual
Perception
through Video Imagery
5.6.1.
Virtual view rendering
. 219
5.6.2.
VRML models
. 220
5.7.
Conclusion
. 220
5.8.
Bibliography
. 221
Chapter
6. 3D
Reconstruction by Active Dynamic Vision
. 225
Éric MARCHAND
and
François CHAUMETTE
6.1.
Introduction: active vision
. 225
6.2.
Reconstruction of
3D
primitives
. 227
6.2.1.
Reconstruction by dynamic vision: a rapid state of the art
. 227
6.2.2.
General principle
. 230
6.2.3.
Some specific cases
. 232
6.2.3.1.
Point
. 232
6.2.3.2.
Line
. 233
6.2.3.3.
Cylinder
. 235
6.2.4. 3D
reconstruction by active vision
. 235
6.2.4.1. 3D
reconstruction by active vision: state of the art
. . 236
6.2.4.2.
Optimal
3D
reconstruction of a primitive
. 237
6.2.5.
Generation of camera movements
. 240
6.3.
Reconstruction of a complete scene
. 243
6.3.1.
Automatic positioning of the camera for the observation of
the scene
. 243
6.3.2.
Scene reconstruction: general principle
. 244
6.3.3.
Local focusing strategy
. 245
6.3.4.
Completeness of reconstruction: selection of viewpoints
. . 247
6.3.4.1.
Calculation of new viewpoints
. 247
6.3.4.2.
Optimization
. 250
6.4.
Results
. 250
6.4.1.
Reconstruction of
3D
primitive: case of the cylinder
. 251
6.4.2.
Perception strategies
. 252
6.4.2.1.
Local exploration
. 252
6.4.2.2.
Total exploration
. 254
6.5.
Conclusion
. 257
6.6.
Appendix: calculation of the interaction matrix
. 258
6.7.
Bibliography
. 259
Part
4. 263
Chapter
7.
Shape Recognition in Images
. 265
Patrick
GROS
and Cordelia
SCHMID
7.1.
Introduction
. 265
Table
of
Contents 11
7.2. State
of the art .
266
7.2.1.
Searching images based on photometric data
. 266
7.2.2.
Search for images based on geometric data
. 267
7.2.3.
Recognition using a
3D
geometric model
. 268
7.2.4.
Recognition using a set of images
. 270
7.3.
Principle of local quasi-invariants
. 270
7.4.
Photometric approach
. 272
7.4.1.
Key points
. 272
7.4.2.
Differential invariants of gray levels
. 273
7.4.3.
Comparison of descriptors with Mahalanobis distance
. 275
7.4.4.
Voting algorithm
. 276
7.4.5.
Semi-local constraints
. 277
7.4.6.
Multi-dimensional indexing
. 278
7.4.7.
Experimental results
. 279
7.4.8.
Extensions
. 282
7.5.
Geometric approach
. 284
7.5.1.
Basic algorithm
. 284
7.5.2.
Some results
. 285
7.5.2.1.
Pairing results
. 285
7.5.2.2.
Results of indexing and recognition
. 286
7.6.
Indexing of images
. 288
7.6.1.
Traditional approaches
. 290
7.6.2.
VA-File and the Pyramid-Tree
. 291
7.6.3.
Some results
. 292
7.6.3.1.
Context of experiments
. 293
7.6.3.2.
First experiment
. 293
7.6.3.3.
Second experiment
. 293
7.6.3.4.
Third experiment
. 294
7.6.4.
Some prospects
. 294
7.7.
Conclusion
. 295
7.8.
Bibliography
. 296
List of Authors
. 301
Index
. 305
For several decades researchers have tried to construct per¬
ception systems based on the registration data from video
cameras. This work has produced various tools that have
made recent advances possible in this area.
The first part of this book deals with the problem of the cali¬
bration and auto-calibration of video captures. It then moves
on to the estimation of the relative object/capture position
when a priori information is introduced (the CAD model of
the object). Finally, the inference of density information and
shape recognition in ¡mages are discussed.
Michel Dhome is Research Director at the
CNRS
and is a
Professor at the University of Clermont-Ferrand, France. |
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| id | DE-604.BV035149570 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:46:09Z |
| indexdate | 2024-07-09T21:26:06Z |
| institution | BVB |
| isbn | 9781848210165 1848210167 |
| language | English French |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016956837 |
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| physical | 307 S. Ill., graph. Darst. |
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| spelling | Perception visuelle par imagerie video Visual perception through video imagery edited by Michel Dhome London ISTE [u.a.] 2009 307 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes index Computer vision Visual perception Vision Videobild (DE-588)4563329-0 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s Videobild (DE-588)4563329-0 s DE-604 Dhome, Michel Sonstige oth Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016956837&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016956837&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Visual perception through video imagery Computer vision Visual perception Vision Videobild (DE-588)4563329-0 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| subject_GND | (DE-588)4563329-0 (DE-588)4129594-8 |
| title | Visual perception through video imagery |
| title_alt | Perception visuelle par imagerie video |
| title_auth | Visual perception through video imagery |
| title_exact_search | Visual perception through video imagery |
| title_exact_search_txtP | Visual perception through video imagery |
| title_full | Visual perception through video imagery edited by Michel Dhome |
| title_fullStr | Visual perception through video imagery edited by Michel Dhome |
| title_full_unstemmed | Visual perception through video imagery edited by Michel Dhome |
| title_short | Visual perception through video imagery |
| title_sort | visual perception through video imagery |
| topic | Computer vision Visual perception Vision Videobild (DE-588)4563329-0 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | Computer vision Visual perception Vision Videobild Maschinelles Sehen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016956837&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016956837&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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