Computer Vision

• Identify relevant methods for solving computer vision problems.
• Implement a chosen computer vision algorithm in e.g. Matlab or Python.
• Carry out a systematic performance analysis of a computer vision algorithm.
• Apply one and two view geometry for estimating points, positions, and surfaces.
• Implement and use linear methods for camera estimation.
• Implement and use camera calibration.
• Implement and apply the RANSAC algorithm.
• Find correspondences between 2D image points and estimate 3D points from these.
• Understand and use image features in computer vision.
• Use common computer vision software libraries.

Methods covered in the course are based on a combination of mathematics, statistics and machine learning, all applied to images from a perspective camera. Students are introduced to theoretic aspects of computer vision methods and obtain practical experience by carrying out exercises, where the methods are implemented in computer programs. It is expected that students participating in this course have experience in programming in Python and experience with fundamental topics and concepts from image analysis.

02502, Experience programming in Python and numpy. Image analysis or similar introductory course that covers introduction to images and operations on images. This includes, among others, morphological operations, filtering, geometric transformations, image registration, classification, and segmentation.

Lectures and exercises

Minimum: 6.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.