COMPUTER VISION

Academic Year 2019/2020 - 1° Year - Curriculum Data Science

Teaching Staff: Sebastiano BATTIATO
Credit Value: 6
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

Oral lectures with several practical sessions

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

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