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Computer vision fundamentals

  • Class 15
  • Practice 30
  • Independent work 105
Total 150

Course title

Computer vision fundamentals

Lecture type

Elective

Course code

21-02-513

Semester

2

ECTS

5

Lecturers and associates

Course overview

This module introduces students to computer vision applications based on deep learning. Deep learning is a trend in research and development in machine learning. Deep learning methods have carried ground-breaking advances in computer vision. They can solve many complex problems of computer vision which were not easily solved by machine learning.

Students who take this module will gain knowledge and skills in deep learning and its application to computer vision, contributing significantly to students’ development as professionals in the fields between software engineering and data science.

Students will learn:
• About image classification algorithms, detection and localization techniques.
• About segmentation methods and transfer learning concepts.
• How to program a software solution based on deep machine-learning concepts.

The module is taught in Python programming language. The module assessment is based on solving a series of smaller practical tasks and on individual student projects. In these projects, students must create a solution based on computer vision.

Literature

Essential reading:
1. Elgendy, M. (2020) Deep Learning for Vision Systems. Shelter Island: Manning Publications.

Recommended reading:
1. Géron, A. (2019) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Sebastopol: O'Reilly Media.

Further reading:
1. Ranjan, S. and Senthamilarasu, S. (2020) Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques. Birmingham: Packt Publishing.

Minimal learning outcomes

  • Apply basic procedures, methods and algorithms of deep learning in the field of image processing and computer vision.
  • Implement classification techniques in the field of computer vision based on deep learning.
  • Implement detection and localization techniques in the field of computer vision based on deep learning.
  • Implement segmentation techniques in the field of computer vision based on deep learning.

Preferred learning outcomes

  • Apply procedures, methods and algorithms of deep learning in the field of image processing and computer vision in solving complex problems.
  • Implement classification techniques in the field of computer vision based on deep learning in solving complex problems.
  • Implement detection and localization techniques in the field of computer vision based on deep learning in solving complex problems
  • Implement segmentation techniques in the field of computer vision based on deep learning in solving complex problems
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