Artificial Vision
A.Y. 2024/2025
Learning objectives
Aim of the course is to give elements which allow to infer knowledge about the real world from digital images or videos. These skills mainly concern the 3D reconstruction of real objects, and the processing and recognition of elements and actions in a scene.
Expected learning outcomes
- Learn the image formation principles
- Learn the techniques to obtai 3D object reconstruction
- Learn machine learning techniques aimed at the identification and recognition of objects and actions in images or videos
- Learn the techniques to obtai 3D object reconstruction
- Learn machine learning techniques aimed at the identification and recognition of objects and actions in images or videos
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
Aim of this course is to examine the fundamental concepts in the field of computer vision, with special focus on the following topics:
* Image formation
- Geometric camera models
- Camera Calibration
- Brightness models
- Color models
* "Early" vision
- Linear Filtering
- Feature extraction
- Stereopsis (binocular vision)
- Structure from Motion (multi-view)
- 3D Registration
* Machine learning
- Linear Neural Networks
- Multilayer perceptrons
- Deep Learning Computation
- Convolutional Neural Networks
- Transformers
- Recurrent Neural networks
- Graph Neural Networks
- Generative models
* Image formation
- Geometric camera models
- Camera Calibration
- Brightness models
- Color models
* "Early" vision
- Linear Filtering
- Feature extraction
- Stereopsis (binocular vision)
- Structure from Motion (multi-view)
- 3D Registration
* Machine learning
- Linear Neural Networks
- Multilayer perceptrons
- Deep Learning Computation
- Convolutional Neural Networks
- Transformers
- Recurrent Neural networks
- Graph Neural Networks
- Generative models
Prerequisites for admission
A good knowledge of the fundamentals of:
- probability and statistics
- signal and image processing
as taught in scientific undergraduate courses.
- probability and statistics
- signal and image processing
as taught in scientific undergraduate courses.
Teaching methods
Lectures and demo in python
Teaching Resources
Web site:
https://github.com/lanzarotti/
Class material:
- Lecture slides
- Software code for exercises
Books:
- D.A. Forsyth, J. Ponce - Computer Vision - A Modern Approach - Pearson, 2nd edition
- C.M. Bishop, H. Bishop - Deep Learning - Foundations and Concepts - Springer
- A. Zhang, Z.C. Lipton, M. Li, A.J. Smola - Dive into Deep Learning
https://github.com/lanzarotti/
Class material:
- Lecture slides
- Software code for exercises
Books:
- D.A. Forsyth, J. Ponce - Computer Vision - A Modern Approach - Pearson, 2nd edition
- C.M. Bishop, H. Bishop - Deep Learning - Foundations and Concepts - Springer
- A. Zhang, Z.C. Lipton, M. Li, A.J. Smola - Dive into Deep Learning
Assessment methods and Criteria
The exam consists of a written test with open-ended questions or a project, which involves submitting a report and the produced code, as well as presenting the project itself.
The evaluation is expressed on a thirty-point scale.
The evaluation is expressed on a thirty-point scale.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Lanzarotti Raffaella
Shifts:
Turno
Professor:
Lanzarotti RaffaellaProfessor(s)