Computer Vision and Intelligent Systems

Objectives

Computer Vision is called to revolutionise industrial automation in smart factories and autonomous driving. Among the most important and powerful sensors in automated factory environments and in self-driving vehicles are vision systems with cameras. The aim of this course is to provide an introduction to computer vision and intelligent systems and their role in the state of the art techniques in the automotive sector. The course should provide the students with:

• General understanding of the state of the art computer vision methods involved in automated factory environments and self-driving systems.
• Ability to implement simple computer vision solutions in a laboratory environment.
• Ability to independently develop a course project.

Learning outcomes

The student knows how to apply the principles of computer vision and digital image processing techniques.

Competencies

Specific skills

• Understand the basic principles of use and programming of computers, operating systems, databases, software applications in engineering, industrial computing and communications networks, and apply this to engineering in general and to the design of connectivity systems in the automotive sector.
• Understand the principles of mathematical theory in order to solve mathematical problems that may arise in engineering and apply knowledge to: linear algebra, geometry, differential geometry, differential and integral calculus, ordinary and partial differential equations, numerical methods, numerical algorithms, statistics and optimisation.
• Work in a multilingual, multidisciplinary environment, and make oral presentations and write reports in English in the field of engineering, in general, and in the automotive sector, in particular.

Basic skills

• Students have the ability to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific and ethical issues.

Content

1. The human visual system and computer vision systems
2. Digital image fundamentals
   • Image representation
   • Colour image processing
3. Image enhancement
   • Spatial domain methods
   • Frequency domain methods
4. Image understanding
   • Feature extraction
   • Pattern recognition
   • Introduction to deep learning

Evaluation

The evaluation of the course follows a continuous assessment methodology through the presentation of a practical assignment, and a course project. An exam will take place at the end of the term to ensure that each student has individually achieved the main objectives of the course. During the last weeks of the course, the students will work on the course project in reduced teams (4-5 people), which will be more ambitious in scope than the practical assignment (developed in reduced teams of 2 people) as it will tackle a real challenge. To develop this project, the students will need to study and deepen into some concepts in an autonomous way. The team members must expose and report regularly on the status of the project. At the end of the course, the teams will publicly defend their project and will deliver a final report.

Final mark = 0.3 · A + 0.4 · P + 0.3 · E*

• A: Assignment (individual evaluation + collective evaluation) (non-retakable)
• P: Final Project (individual + collective evaluation) (non-retakable)
• E: Exam (individual evaluation) (retakable)

Methodology

The course combines master classes and a project based learning (PBL) methodology. The instructor will present the key concepts of each topic during the lectures, which should be consolidated both individually and cooperatively by the students. Such lectures will include hands-on practical exercises. There will also be specific sessions in which the students will work on practical assignments. These assignments will be subsequently delivered as part of a continuous evaluation framework. At the end of the course, a group project will be defined and executed. The project aims to develop technical and interpersonal skills, as well as individual responsibility, while providing a highly motivating context that will allow you to address your specific interests in the field. During the lectures, the working environment will be Matlab®.

Bibliography

Bibliography

• Gonzalez, R.C., and Woods, R.E. (2006). Digital Image Processing (3 ed.). Prentice Hall.

Reading

Teachers will provide complementary bibliography and compulsory reading throughout the course via the Virtual Campus.