Computer Vision

4 ECTS credits
105 h study time

Offer 1 with catalog number 4016979ENR for all students in the 1st semester at a (E) Master - advanced level.

Semester

1st semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Taught in

English

Partnership Agreement

Under interuniversity agreement for degree program

Faculty

Faculteit Ingenieurswetenschappen

Department

Electronics and Informatics

Educational team

David Blinder (course titular)

Activities and contact hours

18 contact hours Lecture
12 contact hours Seminar, Exercises or Practicals
36 contact hours Independent or External Form of Study

Course Content

The goal of computer vision is to develop the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analyzed from a single image or a set of images. Since images are two-dimensional projections of the three-dimensional world, the information is not directly available and must be recovered. This is a very difficult problem given that the inversion is a many-to-one mapping. To recover the information, knowledge about the objects in the scene and projection geometry is required. This course will cover the fundamentals of Computer Vision. The field draws heavily on many subjects including digital image processing, artificial intelligence and computer graphics. The objectives are to develop your understanding of the basic principles and techniques of image understanding, and to develop your skills in the design and implementation of computer vision software.

Course Content:

Refresher on Linear Algebra and Optimization. Basic concepts, terminology, theories, models and methods in the field of Computyer vision. Basic methods of Inverse Problem in computer vision related to 3D reconstruction from Stereo, Motion and Shading as well as Structured Light and Time-of-Flight. A short introduction to data-driven computer vision model - Deep Stereo and Depp Photometric Stereo

Course material

Digital course material (Recommended) : Computer Vision, Algorithms and Applications, R. Szeliski, freely available online: http://szeliski.org/Book/, 2011

Additional info

not applicable

Learning Outcomes

Algemene competenties

Understand and master basic knowledge, theories and methods in computer vision.
Implement basic computer vision algorithms
Develop and evaluate solutions to problems in computer
Identify, formulate and apply computer vision techniques for solving practical problems.
Critically review and assess scientific literature in the field and apply theoretical knowledge to identify the novelty and practicality of proposed methods.
Demonstrate awareness of the current key research issues in computer vision

Grading

The final grade is composed based on the following categories:
Oral Exam determines 75% of the final mark.
PRAC Lab Work determines 25% of the final mark.

Within the Oral Exam category, the following assignments need to be completed:

Oral Exam with a relative weight of 75 which comprises 75% of the final mark. This is a mid-term test.

Within the PRAC Lab Work category, the following assignments need to be completed:

WPO with a relative weight of 25 which comprises 25% of the final mark. This is a mid-term test.

Additional info regarding evaluation

Oral examination: 75%

Exercises: 25%

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context