Advanced Topics in Computer Vision
Datorseende avancerad kurs

FMA315F, 7.5 credits

Valid from: Autumn 2023
Decided by: Maria Sandsten
Date of establishment: 2023-10-03

General Information

Division: Mathematics
Course type: Third-cycle course
Teaching language: English

Aim

This course will cover advanced topics in computer vision with a focus on geometry,

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• Understand advanced techniques in computer vision for geometry, including SLAM, SFM, and visual localization
• Understand and discuss the current state-of-the-art and future directions in computer vision for geometry research
• Be familiar with metrics and benchmarks used for evaluation in the field, to measure performance of State-of-the-art methods

Competences and Skills

For a passing grade the doctoral student must

• Be able to apply advanced techniques in computer vision for geometry, including SLAM, SFM, and visual localization
• Analyze and interpret images and videos for 3D reconstruction and scene understanding
• Develop and implement systems for pose estimation and mapping
• Be able to use metrics and benchmarks to measure performance of developed methods.

Course Contents

Topics covered in the course include simultaneous localization and mapping (SLAM), structure from motion (SFM), and visual localization. Topics will include both traditional methods and recent developments in deep learning-based approaches.

Course Literature

Vetenskapliga artiklar som belyser både historik och nuvarande forskningsfront inom området.

Instruction Details

Types of instruction: Seminars, self-study literature review

Examination Details

Examination format: Seminars given by participants
Grading scale: Failed, pass
Examiner:

Admission Details

Course Occasion Information

Contact and Other Information

Course coordinators: