Computational Vision

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Credits
5
Types
Compulsory
Requirements
This subject has not requirements
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

  • Petia Radeva ( )

Others

  • Laura Igual ( )

Weekly hours

Theory
1.5
Problems
0
Laboratory
1.5
Guided learning
0
Autonomous learning
3

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

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 I
  9. Segmentation II
  10. Texture analysis
  11. Video Segmentation
  12. Object Recognition
  13. Image classification with 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.

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 final exam of theory. Final grade: 60% practicum grade and 40% final exam grade.

Bibliography

Basic:

Addendum

Contents

There are no significant changes.

Teaching methodology

Beyond the adaptation of the course to a mixed (presential-virtual) methodology, there are no significant changes.

Evaluation methodology

There are no significant changes.

Contingency plan

In case of total confinement the classes will switch to virtual synchronous.