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Subject: IMAGE PROCESSING (A.A. 2024/2025)


Course year 3
Teaching units Unit Image Processing
Computer Engineering (lesson)
  • TAF: Compulsory subjects, characteristic of the class SSD: ING-INF/05 CFU: 6
Teachers: Enver SANGINETO
Exam type written
Evaluation final vote
Teaching language Italiano
Contents download pdf download




This course provides the fundamentals of image processing: the acquisition and formation of digital images, the study and choice of optics, sensors and lighting, image processing and analysis algorithms, geometric transforms. The course will mainly be carried out through theory lectures, with a programming part for the implementation of image processing algorithms and the use of advanced libraries for vision systems.

Admission requirements


Course contents

Image acquisition and formation (3 CFU):

- The optics and lenses
- Basics of Optics
- Image quality
- Fixed Focal and Macro Lenses
- 360 ° lenses
- Telecentric lenses
- Scheimpflug principle

- Lighting and lighting geometries
-Why it is important in Machine Vision
- Behavior of the Light
- Light sources
- Lighting geometries
- Interaction between different wavelengths and objects
- Structured lighting

- Rooms
- Bases of Chambers
- Monochromatic and color sensors
- Characteristics of a sensor
- Room parameters
- Communication interfaces
- GeniCam

Image Processing (3 CFU):

- Digital Image Fundamentals
- The color spaces (characteristics and use of the main color spaces, conversion between color spaces)
- Histograms (types of histograms, normalization, color histograms)
- Equalization
- Methodologies and metrics for histogram comparison
- Thresholding (fixed thresholds, automatic thresholds, adaptive thresholds)
- Point and local operators, convolutional filters
- Image Restoration
- Edge detection
- Frequency domain, Fourier transform

Teaching methods

In-presence lessons. The course is delivered in Italian. Working students will find exam preparation material on, including recordings of lessons from a previous academic year. The course also includes some programming exercises on the covered topics.

Assessment methods

Oral exam on the course contents. The goal of the exam is to evaluate the student's theoretical preparation and the ability to understand the topics covered in the course.

Learning outcomes

At the end of the course the student will have acquired the basic knowledge of Image Processing and its algorithms and main concepts, as well as the basic principles concerning lighting, optics and image and video acquisition systems. The analysis and the class discussions of the various topics, as well as the Python programming exercises, will allow the student to achieve the skills necessary to apply the knowledge acquired during the course to problems of practical interest.


- Dispense a cura dei docenti
- Digital Image Processing, 4Th Edition (Rafael C. Gonzalez, Richard E. Woods)
- Alexander Hornberg (eds.) - Handbook of Machine and Computer Vision - The Guide for Developers and Users-Wiley-VCH (2017)