March 14, 2017

Artificial vision a.a. 2016-17

Course goals

The course aims at providing the skills on the main methodologies and techniques required to realize an artificial vision system.

Lessons

Lessons Exercises Laboratories Material
Introduction L1(2h):
Historical introduction to the computer vision systems.
The processing steps of a computer vision system.
Book
Szeliski (Chap. 1)


Slides


Material

Low level processing L2(3h):
Image acquisition, optics and sensors.
Book
Szeliski (Chap. 2.1)


Slides


Material

L3(2h):
Images and colors representation.
Book
Szeliski (Chap. 2.2, 2.3)


Slides


Material

L4 (3h):
Image filtering and processing
Book
Szeliski (Chap. 3.2)


Slides


Material

Ex1(2h):
Introduction to OpenCV

Book


Slides


Material
Code provided by the lecturer

Lab1 (3h):
Exercise on image filtering and processing using OpenCV

Book


Slides


Material
Exercise assigned by the lecturer

Intermediate level processing L5(2h):
Corners and edge detection and contours extraction.
Book
Szeliski (Chap. 4)
Forsyth (Chap. 8)


Slides


Material

L6 (3h):
Features, descriptors and shapes
Book
Szeliski (Chap. 4)


Slides


Material

L7 (2h):
Segmentation
Book
Szeliski (Chap. 5)


Slides


Material

Ex2(2h):
Intermediate level processing using OpenCV

Book


Slides


Material
Code provided by the lecturer

Lab2 (3h):
Exercise on intetmediate level processing using OpenCV

Book


Slides


Material
Exercise assigned by the lecturer

High level processing L8 (3h):
Introduction to machine learning.
NN, KNN, KK’NN classifiers.
Neural networks.

Book


Slides


Material

L9 (2h):
LVQ, back propagation, SVM

Book


Slides


Material

Ex3(2h):
Classifiers using OpenCV

Book


Slides


Material
Code provided by the lecturer

Application L10 (3h):
Viola-Jones algorithm (Haar features, AdaBoost, cascade classifier)

Book


Slides


Material

Lab3 (2h):
Face recognition using OpenCV

Book


Slides


Material
Exercise assigned by the lecturer

Lab3 (2h):
Face recognition using OpenCV

Book


Slides


Material
Exercise assigned by the lecturer

L11 (3h):
Background subtraction and tracking

Book


Slides


Material

Lab4 (2h):
Object detection using OpenCV

Book


Slides


Material
Code provided by the lecturer

Lab5 (2h):
Object tracking using OpenCV

Book


Slides


Material
Exercise assigned by the lecturer

L12 (2h):
Action and gesture recognition

Book


Slides


Material

Lab6 (4h):
Gesture recognition using OpenCV

Book


Slides


Material
Exercise assigned by the lecturer

L13 (2h):
Robot vision

Book


Slides


Material

L14 (2h):
Drone vision

Book


Slides


Material

L15 (2h):
Embedded vision

Book


Slides


Material