Details for Course FMA315F Advanced Topics in Computer Vision

Current Established Course Syllabus

General

Aim

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

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.

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.

Judgement and Approach

Types of Instruction

Examination Formats

Admission Requirements

Assumed Prior Knowledge

Selection Criteria

Literature

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

Further Information

Course code

Administrative Information

1 course syllabus.

Valid from	First hand in	Second hand in	Established
Autumn 2023	2023‑09‑12 14:51:53	2023‑09‑12 15:36:25	2023‑10‑03

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