Computational Vision

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Credits
5
Types
Compulsory
Requirements
This subject has not requirements

Department
UB
This course introduces the main aspects of computational vision, from fundamentals on image formation and basic image operations until scene recognition, going through the main problems in computer vision: segmentation, motion estimation, pattern recognition and object tracking. The latest state-of-the-art methods will be revised for the computer vision problems and methods will be developed to solve some of these problems.

Teachers

Person in charge

  • Laura Igual ( )

Others

  • Petia Radeva ( )

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.

Solvent use of the information resources

  • 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 formation
  3. Linear filters, differential operators, interest points
  4. Clustering and Segmentation
  5. Texture
  6. Descriptors and correspondence. Context Shape.
  7. Motion, optical flow and tracking
  8. Detectors and descriptors, matching based on Sift and Ransac
  9. Pattern Matching
  10. Pattern Classification
  11. Object Tracking
  12. Patch Based Recognition and Part Based Recognition
  13. Composability and visual grammars
  14. Scene recognition

Activities

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.
Theory
0
Problems
0
Laboratory
9
Guided learning
0
Autonomous learning
9
Objectives: 1
Contents:

Practicum deliverable 2

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

Practicum deliverable 2

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

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 on in-class oral presentations and their work in practical assignments. Typically, marks for oral presentations will be awarded on an individual basis, whereas marks for practical assignments will be based on an assessment of the whole group (2 persons per grup). The weighting of the final grade will be proportional to the respective workloads of the two tasks and a final exam.

Bibliografy

Basic:

  • Computer Vision: A Modern Approach - Jean Ponce David A. Forsyth, Prentice Hall , 2011. ISBN: 013608592X, 9780136085928
  • Computer Vision: Algorithms and Applications - Richard Szeliski, , 2011. ISBN: