MSc: Computer Vision

Computer Vision

• Course name: Computer Vision
• Course number: R-03
• Area of instruction: Computer Science and Engineering

Administrative details

• Faculty: Computer Science and Engineering
• Year of instruction: 1st year of MSc
• Semester of instruction: 2nd semester
• No. of Credits: 5 ECTS
• Total workload on average: 180 hours overall
• Frontal lecture hours: 2 hours per week.
• Frontal tutorial hours: 0 hours per week.
• Lab hours: 2 hours per week.
• Individual lab hours: 2 hours per week.
• Frequency: weekly throughout the semester.
• Grading mode: letters: A, B, C, D.

Course outline

This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks, image creation using GANs, and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

Expected learning outcomes

• Apply knowledge of image acquisition, image processing, and image analysis to extract useful information from visual images.
• Design, implement, and document appropriate, effective, and efficient software solutions for a variety of real-world computer vision problems.
• Exploit standard computer vision software libraries in the development of these solutions.

Required background knowledge

Solid knowledge of essential topics in the courses that are mentioned below.

Prerequisite courses

Linear Algebra, Calculus, Probability & Statistics, Computer Programming, Basic Machine Learning.

Detailed topics covered in the course

• Introduction to Computer Vision, Image Acquisition, Basic Image Processing
• Kernels, Morphological operations, Smoothing and Blurring, Lightening and Color spaces
• Gradients, Edge Detection, Contours, Histograms, Labelling Connected Components
• Object Detection, Template Matching, Image Pyramids, Object Detection using HOG and SVM
• Image Classification, Common Machine Learning Algorithms for Image Classification
• Clustering, Bag of Visual Words, Image Pyramids, Image Classification Examples
• Face Detection
• Image Descriptors: Color channel statistics, Moments, Texture, HoGs
• Local features, Key point detectors, Local invariant descriptors, Binary Descriptors
• Video Processing: Moving object detection, background models
• Artificial Neural Nets and Convolutional Neural Nets
• Generative Adversarial Networks
• Case Studies: Research paper implementation and presentation

Textbook

There is no specific text book for this course.

Required computer resources

Students should have laptops. A Mac or Window’s PC capable of running a scientific python development environment.

Evaluation

• Quizzes (40%)
• Mid-term exam (15%)
• Case Study (15%)
• Final exam (30%)