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COMPUTER VISION

CODE 86735
ACADEMIC YEAR 2021/2022
CREDITS
  • 5 cfu during the 1st year of 10635 ROBOTICS ENGINEERING (LM-32) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR INF/01
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    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

    The course aims at providing knowledge on theory and tools on the basics of Computer Vision, for the extraction of semantic and geometric information about a scene from an image or a sequence of images. Topics of interest include: camera models and image formation; camera calibration; connection between 2D images and 3D scene structures; image processing basics as image filtering, local features extraction (edge, corner, blob), including the use of multi-scale image representations; image matching, with reference to classification and retrieval problems; stereo vision and scene depth estimation; motion detection in image sequences, including change detection and optical flow estimation.

    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 hands-on 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 (warping)

    Intensity transformations and spatial filtering (filtering in the frequency domain) 

    Edge and corner detection

    Color image processing

    Hough transforms and image segmentation

    Scale space and blob detection

    Image matching

     

    Part 2 - motion analysis 

    Motion: 3D and 2D motion fields, dense and sparse optical flow. Dominant motion estimation                              

    Tracking with linear dynamic models (Kalman Filter)

     

    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, Homographies

    Stereopsis: epipolar geometry, stereo rectification, depth estimation, 3D reconstruction 

     

    ​Conclusions: Visual Recognition 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

    FABIO SOLARI (President)

    MANUELA CHESSA

    FRANCESCA ODONE

    NICOLETTA NOCETI (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    • 50% continuous assessment  through practical exercises done throughout the semester,
    • 50% from end-semester exam.The final exam consists on a quiz and an oral  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 exam.

    Exam schedule

    Date Time Location Type Notes
    14/01/2022 09:00 GENOVA Scritto
    07/02/2022 09:00 GENOVA Scritto
    13/06/2022 09:00 GENOVA Scritto
    12/07/2022 09:00 GENOVA Scritto
    02/09/2022 09:00 GENOVA Scritto