1. Lecture 1.- Introduction
1.1. Objectives
1.2. Overview
1.3. Contents
1.4. Bibliography
1.5. Evaluation
2. Lecture 2.- Cameras and lenses (2 hours)
2.1. Types of Cameras
2.2. Linear and Matricial sensors
2.3. Progressive and Interlaced
2.4. 1 CCD and 3CCD
2.5. Types of optics
2.6. Focal distance, aperture and field of view
3. Lecture 3.- Rigid body transformations (1 hour)
3.1. Linear algebra
3.2. Points and vectors
3.3. Translations and rotations
3.4. Homogeneous coordinates
3.5. Inverses and Transposes
4. Lecture 4.- Camera calibration (3 hours)
4.1. The pinhole camera
4.2. Intrinsic and extrinsic parameters
4.3. Computing the calibration matrix
4.4. Accuracy Evaluation
5. Lecture 5.- Image primitives (2 hours)
5.1. Image filtering and enhancing
5.2. Convolution
5.3. Interest point detectors
5.4. Similarity measures
6. Lecture 6.- Planar transformations (3 hours)
6.1. A hierarchy of transformations: Euclidean, Similarity, Affine, Projective
6.2. Calibrated and uncalibrated homographies
6.3. Computing the homography matrix
6.4. Applications: Mosaicing, Video stabilization
7. Lecture 7.- Outlier rejection (1 hour)
7.1. Probabilistic methods
7.2. Least Median of Squares
7.3. Random Sampling Consensus
8. Lecture 8.- Reconstruction from 2 views (2 hours)
8.1. The principle of Triangulation
8.2. Epipolar geometry
8.3. Computing the Fundamental matrix
8.4. Accuracy Evaluation
8.5. Experimental Results
9. Lecture 9.- Laser projection (2 hours)
9.1. The principle of laser scanning
9.2. State of art
9.3. Peak detection
9.4. Calibration
9.5. Steps to implement a laser scanner
10. Lecture 10.- Pattern Projection techniques (2 hours)
10.1. The principle of codification
10.2. State of art
10.3. Time multiplexing, spatial codification, direct codification
10.4. Steps to implement a pattern projection technique