OVERVIEW

The course is about image processing and computer vision techniques for 3D static and dynamic scene interpretation and to discuss applications  to object tracking, depth perception, object recognition and automatic guidance 

AIMS AND CONTENT

LEARNING OUTCOMES

This course presents the fundamentals in computer vision. Topics include camera modelling, camera calibration, image processing, pose estimation, multi view geometry, visual tracking, and vision based calibration.

AIMS AND LEARNING OUTCOMES

The aim of the course is to provide a broad introduction to different core aspects of computer vision, including  camera modelling, camera calibration, image processing, pose estimation, multi view geometry, visual tracking, and vision based calibration.

At the end of the course the student will be able to understand the main theoretical concepts and to design and implement classical computer vision algorithms. The course will also provide an overview of the main application domains, with a special reference to the robotics scenario.

PREREQUISITES

background knowledge on linear algebra and calculus; basic programming skills

TEACHING METHODS

• theoretical classes followed by practical (step-by-step) activities

SYLLABUS/CONTENT

Introduction to computer vision for robotics applications

Part 1 - image processing fundamentals

• Digital image fundamentals: sensing and acquisition, sampling and quantization, basic operations
• Intensity transformations and spatial filtering (filtering in the frequency domain) 
• Edge and corner detection
• Color image processing
• Hough transforms and image segmentation
• Early vision: feature detection and regularization. Optimal filters and multichannel representations (Gabor functions)

Part 2 - motion analysis and navigation

• Motion: 3D and 2D motion fields, dense and sparse optical flow. Dominant motion estimation                              
• Tracking with linear dynamic models (Kalman Filter)
• Autonomous navigation

Part 3 - geometry

• 3D computer vision fundamentals
• The geometry of image formation: review of projective geometry (basic), projective transformations, camera models and single view geometry, camera calibration
• Stereopsis: epipolar geometry, stereo rectification, depth estimation, 3D reconstruction 

​Conclusions: image matching and  image retrieval;  introduction to object and action recognition methods in HRI

RECOMMENDED READING/BIBLIOGRAPHY

Recommended texts:

• R.C. Gonzalez and R.E. Woods, Digital image processing, Prentice-Hall, 2008.
• E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall,  1998.

Further readings: Material distributed by lecturers through the Aulaweb portal

TEACHERS AND EXAM BOARD

Exam Board

NICOLETTA NOCETI (President)

FRANCESCA ODONE (President)

FABIO SOLARI (President)

RENATO UGO RAFFAELE ZACCARIA (President)

MANUELA CHESSA

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

• 30% continuous assessment  through practical exercises done throughout the semester,
• 70% from end-semester exam.The final exam consists in a two-hour written examination covering all the topics presented in the course. It is not allowed to consult books, notes, or other written material. 

ASSESSMENT METHODS

The course is organized in theory classes and practical (hands-on) classes.

Practical activities cover about 1/3 of the course. The goal of such activities is presented in class by the instructors, and should be completed by the students as a homework. They can be carried out individually or in groups; some of them are associated with an assignment and constitute a continuous assessment of the student's work.

The final assessment of the theory part is carried out through a final written exam.

Exam schedule