Fundamentals of Computer Vision with Deep Learning
The course is part of the programme MAISTR (hh.se/maistr) where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at antagning.se.
About the course Fundamentals of computer vision with deep learning, 5 credits
Who is this course for?
This course presents computer vision and deep learning techniques. It is designed for students with a background in computer science who want to gain additional skills in how machine learning is applied in computer vision.
What will you learn from this course?
You will learn about computer vision concepts, both from a theoretical and practical perspective. The primary content includes: Image acquisition, representation, and transformation. Low-level vision (edges, corners, lines, and circles detection). Feature extraction using deep neural networks and transfer learning. Image pattern classification. Computer vision applications, including: Facial image analysis, In-vehicle vision system (driver drowsiness), Robot vision systems (human emotion and intention detection).
Image acquisition, sampling (pixels) and representation (histogram, color spaces, Fourier Transform)
Image transformations: local & global operators, convolution, filtering (smoothing, sharpening)
Low-level Vision: edges, corners, lines and circles detection, scalar product
Feature extraction and classification:
- Feature Extraction
- Deep Learning and Transfer Learning
- Image Pattern Classification
Computer Vision applications:
- Facial analysis: detection and recognition
- In-vehicle vision system: driver drowsiness
- Robot vision systems: human emotion and intention detection
Degree of Bachelor of Science in Computer Science and Engineering 180 credits. Programming 7.5 credits and Mathematics (Linear Algebra, Multi-variable Calculus) 7.5 credits.
Number of gatherings:
Language of instruction:
Teaching is in English.