Computational Vision

Credits
5
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS;UB
This course introduces the main aspects of computational vision, from fundamentals on image formation and basic image operations until object recognition, going through the main problems of computer vision: segmentation,keypoint extraction, pattern recognition and face recognition. The classical and the latest state-of-the-art methods will be revised for the computer vision problems and methods will be used to solve some of these problems.

Teachers

Person in charge

Others

Weekly hours

Theory
1.5
Problems
0
Laboratory
1.5
Guided learning
0
Autonomous learning
5.33

Competences

Generic Technical Competences

Generic

  • CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.
  • CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.

    • Technical Competences of each Specialization

    Academic

  • CEA6 - Capability to understand the basic operation principles of Computational Vision main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA7 - Capability to understand the problems, and the solutions to problems in the professional practice of Artificial Intelligence application in business and industry environment.

    • Professional

  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.

    • Transversal Competences

    Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

    • Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

    • Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

    • Analisis y sintesis

  • CT7 - Capability to analyze and solve complex technical problems.

    • Objectives

    1. Develop practicum of computational vision.
      Related competences: CT3, CT4, CT6, CEP3, CEP5,
    2. Reach the basic and advanced knowledge of computational vision.
      Related competences: CT7, CEA6, CEA7, CG1, CG3,

    Contents

    1. Introduction to Computational Vision
    2. Image Processing
    3. Edges and contours detection
    4. Feature detection
    5. Feature Matching
    6. Face detection
    7. Face recognition
    8. Segmentation
    9. Classificiation by CNNs
    10. Visualization and interpretability
    11. Detection by CNNs
    12. Atention and transformers
    13. Segmentation by CNNs

    Activities

    Activity Evaluation act


    Practicum deliverable 1

    This activity consists of delivering the code and reprt corresponding to a serie of exercices posed during the first bloc of the course.
    Objectives: 1
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    9h
    Guided learning
    0h
    Autonomous learning
    9h

    Practicum deliverable 2

    This activity consists of delivering the code and reprt corresponding to the problem posed during the second bloc of the course.
    Objectives: 1 2
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    9h
    Guided learning
    0h
    Autonomous learning
    9h

    Practicum deliverable 2

    This activity consists of delivering the code and reprt corresponding to the problem posed during the third bloc of the course.
    Objectives: 1 2
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    9h
    Guided learning
    0h
    Autonomous learning
    9h

    Teaching methodology

    The course will be divided in a series of theory and practical sessions:

    - Participatory theory sessions in which new concepts are introduced and discussed between students. Group discussion is strongly encouraged. Textbook chapters and research papers will be provided to facilitate debate and exchange of ideas.

    - Practical sessions are devoted to solve problems, designing methods and developing prototypes. These sessions allow students to put into practice previously introduced concepts to gain further insight.

    In principle, we expect to follow the in-person teaching model for the 2022-23 academic year.

    Moreover, class material should use an inclusive language and include bibliographical references authored by women (and make them visible).

    Evaluation methodology

    Students will be assessed based on their work in practical tasks (delivery of practices in groups of 2 students) and a final exam of theory. The weighting of the final mark will be proportional to the respective workloads of the practical tasks and the exam of theory. The theory exam will be divided in two midterm exams. Students who fail the first midterm exam will be examined on the entire course during the second part. Final grade: 50% practicum grade and 50% exam grade. To pass the subject, it is necessary to pass the theoretical and practical parts separately, as well as each partial exam separately. If any of the partial exams are failed, the student will take a final exam on all the material.

    Bibliography

    Basic