COMPUTER VISION
Academic Year 2019/2020 - 1° Year - Curriculum Data ScienceCredit Value: 6
Scientific field: INF/01 - Informatics
Taught classes: 24 hours
Exercise: 24 hours
Term / Semester: 2°
Learning Objectives
To provide an introduction to the computer vision field by presenting the main theroretical basis and related applicative scenario.
Both theoretical and prcatical aspects will be introduced
Course Structure
Oral lectures with several practical sessions
Detailed Course Content
Image formation
Low Level Vision: Filters and Features: Edges, Texture, Laplacian Pyramid,Corner Detection (Harris, …), SIFT
Mid level vision: Segmentation (Thresholding, Seeded Region Growing, Statistical Region Merging, ..)
CV application CBIR Retrieval, Video Stabilization, Face detection and Recognition
Textbook Information
E. Trucco, A. Verri, “Introductory Techniques for 3-D Computer Vision”, Prentice Hall, 1998
G. Bradski, A. Kaehler, “Learning OpenCV Computer Vision with the OpenCV Library” O'Reilly Media, 2008;
Mubarak Shah, "Fundamentals of Computer Vision" (pdf), 1997
R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision”, 2004;
D. A. Forsyth, J. Ponce, “Computer Vision a Modern Approach”, Prentice Hall PTR, 2002;
Richard Szeliski, Computer Vision: Algorithms and Application, Springer 2010