- The Four "R"s of Computer Vision

- What is CV?

- CV Applications and Examples?

- Geometry and Image Formation

- Light and Color, Cameras and Optics, Pixels and Image Representations

- Camera Calibration

- Epipolar Geometry

- Signals and Systems

- Linear Systems, Spatial and Frequency Domains, Convolution and Filters, Edge Detection

- Neural Networks and Deep Learning (DL)

- Perceptron and Feed-Forward Networks

- Cost Functions Optimisation, Gradient Descend, Retropropagation Algorithm

- DL-layers, CNNs

- Object Detection

- AdaBoost detector, Hough Transform, DL-based Detectors

- Semantic Segmentation

- DL-segmentation, U-Net

- Image Classification and Recognition

- Nearest Neighbour Classification, Linear Classification, Support Vector Machines, DL-classification

- Multiple Views and Motion

- Stereo Correspondence, Optical Flow

- Experimental Setup and Performance Assessment

- ROC Analysis

- R. Szeliski. Computer Vision: Algorithms and Applications. Springer, ISBN: 978-1848829343, 2021.

- E. R. Davies. Computer Vision: Principles, Algorithms, Applications, Learning. Academic Press, ISBN: 978-0128092842, 2018.

- D. Forsyth and J. Ponce. Computer Vision: A Modern Approach (2nd Edition), Pearson Publishing, ISBN: 978-0136085928, 2012.

- Assiduity (A) To get approved at this course, students should attend to - at least - 80% of the theoretical and practical classes;

- Practical Project (P) The practical projects of this course weights 50% (10/20) of the final mark.

- To get approved at the course, a minimal mark of 5/20 should be obtained in the practical project part;

- The pratical project mark is conditioned to an individual presentation and discussion by each student;

- Written Test (F) ?, ? ?th, 2024, ?:00. Room ?.?

- Mark (M) M = (A >= 0.8) * (P * 10/20 + F * 10/20)

- Admission to Exams Students with M >= 6 are admitted to final exams

- The practical projects mark is considered in all exam epochs;