Skip to Main Content (Press Enter)

Logo UNIPV
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Professioni
  • Persone
  • Pubblicazioni
  • Strutture

UNIFIND
Logo UNIPV

|

UNIFIND

unipv.it
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Professioni
  • Persone
  • Pubblicazioni
  • Strutture
  1. Insegnamenti

509500 - SIGNAL AND IMAGE PROCESSING - MOD. 1

insegnamento
ID:
509500
Durata (ore):
28
CFU:
3
SSD:
SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Anno:
2024
  • Dati Generali
  • Syllabus
  • Corsi
  • Persone

Dati Generali

Periodo di attività

Secondo Semestre (03/03/2025 - 13/06/2025)

Syllabus

Obiettivi Formativi

The course aims to give students the theoretical and practical skills for the design and development of algorithms for the processing of digital images.

Prerequisiti

Knowledge acquired in previous courses in mathematics.

Metodi didattici

The image processing course is based on lectures, examples, exercises and analysis of case studies of digital image processing applications

Lectures (hours/year in lecture theatre): 16
Practical classes (hours/year in lecture theatre): 12
Workshops (hours/year in the lab): 0

The lectures are given using slides.
The practical classes consist in the solution of MATLAB/Python exercises related to the couse content.

Verifica Apprendimento

The final exam is a written test devoted to score the student knowledge by means of a mixture of open-ended and closed-ended questions, covering the course program.

Some, non mandatory, assignments will be provided. Submitting them will provide extra points on the final evaluation.

The minimum score to pass the exam is 18, the top score is 30 cum laude.

Testi

Suggested book: Digital Image Processing, 4rd Edition, Gonzalez & Woods http://www.imageprocessingplace.com/index.htm

Any other versions of the book is acceptable.

PDF of the slides will be provided by the professors.

Contenuti

1 A background on visual perception, human vision vs. artificial vision, color perception. Image sampling and quantization.

2 Image enhancement using intensity transformation functions.

3 Spatial image filtering using liner and non-liner filters.
4 Color spaces. Color image processing.

5 Texture analysis

6 Supervised and unsupervised pixel classification

Lingua Insegnamento

INGLESE

Corsi

Corsi

ARTIFICIAL INTELLIGENCE 
Laurea
3 anni
No Results Found

Persone

Persone (2)

BUZZELLI MARCO
Docente
SCHETTINI RAIMONDO
Docente
No Results Found
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 25.4.2.0