- •CONTENTS
- •PREFACE
- •Abstract
- •1. Introduction
- •2.1. Differential Geometry of Space Curves
- •2.2. Inverse Problem Formulation
- •2.3. Reconstruction of Unique Space Curves
- •3. Rigid Motion Estimation by Tracking the Space Curves
- •4. Motion Estimation Using Double Stereo Rigs
- •4.1. Single Stereo Rig
- •4.2. Double Stereo Rigs
- •5.1. Space-Time or Virtual Camera Generation
- •5.2. Visual Hull Reconstruction from Silhouettes of Multiple Views
- •5.2.1. Volume Based Visual Hull
- •5.2.1.1. Intersection Test in Octree Cubes
- •5.2.1.2. Synthetic Model Results
- •5.2.2. Edge Base Visual Hull
- •5.2.2.1. Synthetic Model Results
- •Implementation and Exprimental Results
- •Conclusions
- •Acknowledgment
- •References
- •Abstract
- •Introduction: Ocular Dominance
- •Demography of Ocular Dominance
- •A Taxonomy of Ocular Dominance
- •Is Ocular Dominance Test Specific?
- •I. Tests of Rivalry
- •II. Tests of Asymmetry
- •III. Sighting Tests
- •Some Misconceptions
- •Resolving the Paradox of Ocular Dominance
- •Some Clinical Implications of Ocular Dominance
- •Conclusion
- •References
- •Abstract
- •1. Introduction
- •2. Basic Teory
- •3. Bezier Networks for Surface Contouring
- •4. Parameter of the Vision System
- •5. Experimental Results
- •Conclusions
- •References
- •Abstract
- •Introduction
- •Terminology (Definitions)
- •Clinical Assessment
- •Examination Techniques: Motility
- •Ocular Motility Recordings
- •Semiautomatic Analysis of Eye Movement Recordings
- •Slow Eye Movements in Congenital Nystagmus
- •Conclusion
- •References
- •EVOLUTION OF COMPUTER VISION SYSTEMS
- •Abstract
- •Introduction
- •Present-Day Level of CVS Development
- •Full-Scale Universal CVS
- •Integration of CVS and AI Control System
- •Conclusion
- •References
- •Introduction
- •1. Advantages of Binocular Vision
- •2. Foundations of Binocular Vision
- •3. Stereopsis as the Highest Level of Binocular Vision
- •4. Binocular Viewing Conditions on Pupil Near Responses
- •5. Development of Binocular Vision
- •Conclusion
- •References
- •Abstract
- •Introduction
- •Methods
- •Results
- •Discussion
- •Conclusion
- •References
- •Abstract
- •1. Preferential Processing of Emotional Stimuli
- •1.1. Two Pathways for the Processing of Emotional Stimuli
- •1.2. Intensive Processing of Negative Valence or of Arousal?
- •2. "Blind" in One Eye: Binocular Rivalry
- •2.1. What Helmholtz Knew Already
- •2.3. Possible Influences from Non-visual Neuronal Circuits
- •3.1. Significance and Predominance
- •3.2. Emotional Discrepancy and Binocular Rivalry
- •4. Binocular Rivalry Experiments at Our Lab
- •4.1. Predominance of Emotional Scenes
- •4.1.1. Possible Confounds
- •4.2. Dominance of Emotional Facial Expressions
- •4.3. Inter-Individual Differences: Phobic Stimuli
- •4.4. Controlling for Physical Properties of Stimuli
- •4.5. Validation of Self-report
- •4.6. Summary
- •References
- •Abstract
- •1. Introduction
- •2. Algorithm Overview
- •3. Road Surface Estimation
- •3.1. 3D Data Point Projection and Cell Selection
- •3.2. Road Plane Fitting
- •3.2.1. Dominant 2D Straight Line Parametrisation
- •3.2.2. Road Plane Parametrisation
- •4. Road Scanning
- •5. Candidate Filtering
- •6. Experimental Results
- •7. Conclusions
- •Acknowledgements
- •References
- •DEVELOPMENT OF SACCADE CONTROL
- •Abstract
- •1. Introduction
- •2. Fixation and Fixation Stability
- •2.1. Monocular Instability
- •2.2. Binocular Instability
- •2.3. Eye Dominance in Binocular Instability
- •3. Development of Saccade Control
- •3.1. The Optomotor Cycle and the Components of Saccade Control
- •3.4. Antisaccades: Voluntary Saccade Control
- •3.5. The Age Curves of Saccade Control
- •3.6. Left – Right Asymmetries
- •3.7. Correlations and Independence
- •References
- •OCULAR DOMINANCE
- •INDEX
28 |
Hossein Ebrahimnezhad and Hassan Ghassemian |
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−f x |
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x 0 |
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(Rwc |
)−1 −(Rwc |
)−1 Tcw |
(40) |
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P = |
0 |
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−f y |
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y 0 |
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0 |
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0 |
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1 |
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[ |
x |
im |
, y |
,1 |
T |
P X w |
,Y w , Z w ,1 T |
(41) |
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im |
] |
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Where, Rcw and Tcw are the rotation and translation of the camera coordinate system
to the world coordinate system, respectively. fx and fy are the focal length in x and y direction and x0, y0 are the coordinates of principal point in the camera plane. From Eq.29, we can get:
W(n ) = M |
n −1 |
W(n −1) = M |
n −1 |
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M |
M W(1) |
(42) |
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2 |
1 |
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By multiplying the projection matrix to the motion matrix, a new projection matrix is deduced for any sequence. This matrix defines a calibrated virtual camera for that sequence as:
P(n ) = P (Mn −1 M2 M1 ) |
(43) |
The matrix P(n) , which produces a new silhouette of the object, projects any point in the world coordinate to the image plane of the virtual camera n as:
(n) (n) T |
(n) |
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w w w |
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T |
(44) |
xim , yim ,1 P |
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X |
,Y , Z ,1 |
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In fact, by generating the virtual cameras, the moving object and fixed camera system is substituted by the fixed object and moving camera that moves in the opposite direction.
5.2. Visual Hull Reconstruction from Silhouettes of Multiple Views
In the resent years, shape from silhouette has been used widely to reconstruct three dimensional shape of an object. Each silhouette makes one cone in the space, with the camera center. Using more cameras from different views of object, more cones are constructed. The visual hull is defined as a shared volume between these cones. There are two conventional approach to extract the visual hull: voxel