MAS512 Computer Vision and Embedded AI
- ECTS Credits:
- 7.5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Ajit Jha
- Lecture Semester:
- Autumn
- Teaching language:
- English
- Duration:
- 1 term
The course is connected to the following study programs
Teaching language
EnglishRecommended prerequisites
ING100-G Programming and ICT Security or equivalent.
Course contents
IMAGE PROCESSING - image filtering, image segmentation, blob detection in images, image representation, transformation between different color spaces, detect and localize landmarks, Fourier transformations, convolution
CAMERA MODELING AND CALIBRATION - Modeling the camera with a camera model, calibrate camera and get the camera matrix and distortion parameters,
3D VISION - SENSORS – LIDAR, RADAR - Lidar, Radar , principles of different sensors for measuring depth, distance, velocity, imaging, point cloud processing, radar signal processing
ALGEBRA AND STOCHASTIC THEORY- Linear Algebra, Probability, Stochastic Theory
MACHINE LEARNING - machine learning architectures (including CNN), configuring and training artificial neural networks, deploying trained artificial neural networks on embedded hardware, RL.
Learning outcomes
On successful completion of the course, the student should be able to:
-
describe a camera model and be able to calibrate a camera to determine the camera matrix that describes the mapping from 2D image to the 3D world
-
use image processing techniques to filter the image and detect objects in the image
-
use sensors such as lidar and radar to make 2D or 3D scans of objects and environments and perform point-cloud processing such as pose estimation
-
configure, train, and use artificial intelligence (AI) algorithms for use in vision applications and
-
select suitable hardware platform for a given AI task, and deploy a pre-trained neural network to the selected platform.
Examination requirements
Students must pass the compulsory assignments in order to take the examination. Information about compulsory assignments will be given in Canvas by the start of the semester.
Teaching methods
Lectures, exercises, laboratory exercises and or project work. Estimated work load for the average student is approximately 200 hours.
Evaluation
The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.
Offered as Single Standing Module
Yes
Assessment methods and criteria
Portfolio consisting of tests and written assigments. Graded assessment. More information about the portfolio will be given in Canvas. There will not be arranged a postponed exam for the portfolio.