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COMPUTATIONAL VISION

CODE 90539
ACADEMIC YEAR 2022/2023
CREDITS
  • 6 cfu during the 1st year of 10852 COMPUTER SCIENCE (LM-18) - GENOVA
  • 6 cfu during the 1st year of 9011 MATEMATICA(LM-40) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR INF/01
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 2° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course offers an introduction to state-of-the-art methods for visual data analysis. In particular it deals with image and video understanding.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    Learning how to represent image content adaptively by means of shallow or deep computational models and biologically-inspired hierarchical models, and how to tackle image classification and categorization problems.

    AIMS AND LEARNING OUTCOMES

    Students will be provided with an an overview of state-of-the-art methods for modeling and understanding the semantics of a scene. Students will get acquainted with the problem of representing the image content adaptively by means of shallow or deep computational models. Then it will address image classification and categorization problems. Possible extensions to depth and motion information will also be discussed.

    Students will be involved in project activities.

    PREREQUISITES

    Calculus and linear algebra.

    Digital image processing and machine learning principles.

    TEACHING METHODS

    Theoretical classes complemented by practical activities

    SYLLABUS/CONTENT

    Course content

    • Elements of classical Computational Vision
      • Review of image processing: image  filtering, feature detection, ...
      • Image matching: feature detection, description and feature similarity between image pairs
      • Multi-scale and  multi-resolution representations
      • Motion analysis  and optical flow
    • Computational Vision and Machine Learning algorithms
      • Bag-of-words representations and image classification
      • Sparse coding on fixed over-complete dictionaries: application face detection
      • Unsupervised segmentation and super-pixel computation
    • ​Computational Vision and Deep Learning:
      • Principles of Deep Learning e Convolutional Neural Networks
      • Convolutional methods for multi-object detection
      • GANs: principles and applications
    • Projects and use cases

    RECOMMENDED READING/BIBLIOGRAPHY

    material provided by the instructors (slides and papers), see course Aulaweb page

    additional reference online book http://szeliski.org/Book/

    TEACHERS AND EXAM BOARD

    Exam Board

    FRANCESCA ODONE (President)

    NICOLETTA NOCETI

    LORENZO ROSASCO (President Substitute)

    ANNALISA BARLA (Substitute)

    ALESSANDRO VERRI (Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    • 30% homework and live participation 
    • 50% project (in groups)
    • 20% theory oral  

    ASSESSMENT METHODS

    • timely delivery of assignments
    • active participation in class and on the online students forum (aulaweb)
    • final project on a use-case (datathon-like) and presentation of the obtained results in a seminar
    • oral exam