Computer Vision (Master) - 491115
Course Title
Computer Vision (Master)
Course Number
491115
Instructor Name
Anas Toma
Contact Information
[email protected]
Semester(s) and academic year(s)
Second Semester 2022
Compulsory / Elective
Compulsory
Course Description
  • Introduction and concepts
    • Human vision, Cameras, Image formation and models, Color
  • Image processing and filtering
    • Basic operations, convolution, morphological processing, Spatial and frequency domains
  • Image resampling
  • Segmentation
    • Component detection, thresholding, and region segmentation
  • Feature description and matching
    • Feature extraction, detection and matching
  • Recognition
    • Basic classification concepts
    • Deep learning: convolutional neural networks
  • Stereo vision
  • Optical flow
    • Motion tracking
  • The 3-D world
  • Applications: Face detection and recognition, surveillance and vehicle vision systems, etc.
Course Objectives

This course introduces the main concepts and techniques used in computer vision. Students explore the human vision system and its relation to the components of computer vision systems. This includes: image capturing, forming and processing images and identifying its components. Students also study motion tracking and stereo vision. The techniques will be explored using real-world problems and different programming libraries such as OpenCV, Scikit-Learn, TensorFlow and Keras.

Intended learning Outcomes and Competences
  • Know the fundamentals of digital image and vision.
  • Explain basic theories and techniques in computer vision.
  • Understand and design image processing and enhancement techniques in both spacial and frequency domain.
  • Explain the concepts of image representation and recognition.
  • Understand and utilize different machine learning techniques that can be used for recognition.
  • Grasp the principles of stereo vision and motion tracking.
  • Design and implement computer vision algorithms for different applications.
  • Contribute to industrial sector by solving real-world problems.
Textbook and References

Textbooks:

  • Computer Vision: Principles, Algorithms, Applications, Learning, 5th Edition. E. R. Davies. eBook ISBN: 9780128095751. Hardcover ISBN: 9780128092842.
  • Computer Vision: Algorithms and Applications, 2nd Edition. Richard Szeliski.
  • Digital Image Processing, 4th Edition. Rafael C. Gonzalez and Richard E. Woods. ISBN-10: 1-292-22304-9.
  • Deep Learning for Vision Systems. ISBN 9781617296192.

Tools:

Assessment Criteria
Activity Percent (%)
Midterm exam 30%
Project/ Homework/ Presentation 30%
Final exam 40%