Contents
Topic 1. Introduction to visual perception (2 hours)
Human vision. Image interpretation: brain vs. computer. Phases of image
processing. CCD sensors. Type of cameras: matricial, linear, 1 CCD, 3
CDD, Analog, Digital.
Topic 2. Camera modelling and calibration (2 hours)
Camera modelling, camera calibration: intrinsic and extrinsic
parameters, stereo vision, epipolar geometry, fundamental matrix. Example: robot localization and 3D mapping.
Topic 3. Motion estimation. (4 hours)
Trinocular stereovision. Deriving homographies from the projection matrix. Robust estimators. Aplications: motion estimation through mosaicking. Derivation of extrinsic parameters.
Topic 4. The correspondence problem. (2 hours)
Detection of interest points. Finding correspondences. Similarity measurements. Aplying epipolar geometry.
Topic 5. 3D reconstruction using laser range finders. (2 hours)
Laser beam calibration. Subpixel slit detection. Scanning. 3D reconstruction. Examples.
Topic 6. Structured light (2 hours)
Pattern projection. Pattern coding. Time multiplexing. Spatial neighborhood. Direct codification. Designing and Implementing an optimal pattern.
Bibliography
[1] Toscani. Systèmes de Calibration et Perception du Mouvement
en Vision Artificielle. Thèse Université Paris Sud, 1987.
[2] Ayache. Artificial Vision for Mobile Robots : Stereo Vision
and Multisensory Perception. The MIT Press, 1991.
[3] Faugeras. Three-Dimensional Computer Vision : A Geometric Viewpoint.
The MIT Press, 1993.
[4] Haralick and L.G. Shapiro. Computer and Robot Vision, Vol I
and II. Addison-Wesley Publishing Company, 1993.
[5] Articles de recerca obtinguts en congresos i revistes especialitzades.
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