The purpose of the Image Analysis and Computer Vision course is to study both the foundations on image formation,
image analysis, and the methodology underlying the solution techinques to the main problems involved.
Image analysis addresses the extraction of the content of one or several images, in order to obtain a representaton of the observed 3D scene.
Optical aspects, geometrical ones, and algorithmic aspects are studied,
as well as aspects connected to signal processing and data analysis.
A project allows to closely examine, possibly from a practical point of view, one or more of the discussed topics.
1. Camera sensors: transduction, optics, geometry, distortion
2. Basics on Projective geometry: modelling basic primitives (points, lines, planes, conic sections, quadric surfaces) and projective spatial transformations and projections.
3. Camera geometry, and single view analysis: localization of 3D models.
4. Multi-view analysis: 3D reconstruction, self-calibration.
5. Linear filters and convolutions, space-invariant filters, Fourier Transform, sampling and aliasing.
6. Nonlinear filters: image morphology and morphology operators (dilate, erode, open, close), median filters.
7. Edge detection and feature detection techniques. feature matching and feature tracking along image sequences.
8. Image segmentation, contour segmentation, clustering, Hough Transform, Ransac (random sample consensus).
9. Object tracking, object recognition, and other applications.