Coursebooks 2017-2018

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Image and video processing

EE-550

Lecturer(s) :

Ebrahimi Touradj

Language:

English

Summary

This course covers fundamental notions in image and video processing, as well as covers most popular tools used, such as edge detection, motion estimation, segmentation, and compression. It is composed of lectures, laboratory sessions, and mini-projects.

Content

Introduction, acquisition, restitution
Two-dimensionnal signals and systems, Elementary signals, Properties of two-dimentional Fourier transform, Discretization (spatial and spatio-temporal artefacts), Two-dimensional digital filters, Two-dimensional z-transform, Transfer function. Captors, monitors, printers, half-toning, color spaces.
Multi-dimensional filtres
Design of Infinite Impulse Response and Finite Impulse Response filters, Implementation of multi-dimensional filters, Directional decomposition and directional filters, M-D Sub-band filters, M-D Wavelets.
Visual perception
Neural system, Eye, Retina, Visual cortex, Model of visual system, Special effects, Mach phenomena and lateral inhibition, Color, Temporal vision.
Contour and feature extraction, segmentation
Local methods, Region based methods, Global methods, Canny, Mathematical morphology. Segmentation, Motion estimation
Visual information coding
Overview of the information theory and basics of rate-distortion, Conventional techniques : predictive coding, transform coding, subband coding, vector quantization, Advanced methods : multiresolution coding, perception based coding, region based coding, directional coding, fractals, Video coding : motion compensation, digital TV, High definition TV. Standards: JPEG, MPEG, H.261, H.263

Keywords

Contour detection, motion estimation, segmentation, human visual system, image compression, video compression

Learning Prerequisites

Required courses

Fundamental notions of signal processing

Recommended courses

Signal processing for communication

Important concepts to start the course

Sampling, quantization, transforms, programming, algorithms, systems

 

Learning Outcomes

By the end of the course, the student must be able to:

Transversal skills

Teaching methods

Ex cathedra, laboratory sessions, mini-projects

Expected student activities

Written report of laboratory sessions, oral presentation of mini-projects,  comprehension of various notions presented during the course, resolve simple problems of image and video processing.

 

Assessment methods

Laboratories, mini-project, oral exam

Supervision

Office hours No
Assistants Yes
Forum Yes
Others Students are encouraged to ask for appoitment with the professor any time outside of teaching hours

Resources

Bibliography

handouts of image and video processing course

Fundamentals of Digital Image Processing, A. K. Jain

 

Ressources en bibliothèque
Moodle Link

Prerequisite for

Semester projects , master thesis projects, doctoral thesis

In the programs

Reference week

 MoTuWeThFr
8-9  BC04
CO5
BC04
CO4
 
9-10   
10-11  BC04
CO5
  
11-12    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German