Master Thesis Project – Estimating intrinsic camera parameters using a CNN

Key Skills

CNNs, Computer Vision (CV), Design, Machine learning techniques, Project management

Job Description

Background

As we move towards solutions where our CNN does predictions in world coordinates for Computer Vision applications, the camera calibration becomes even more important to reach the desired functional performance. We want to explore new methods for estimating camera parameters taking changing camera properties and different types of cameras into consideration.

Purpose

By evaluating how well the CNN can estimate the camera calibration parameters online we increase our understanding of potential performance improvements and possibilities to relax requirements on the calibration accuracy at vehicle production.

Learning Objectives

• Improved knowledge about how well a CNN can estimate camera calibration parameters online.
• Better understanding of how the CNN can be trained to perceive the world in 3D, for example if estimating the camera calibration parameters as an auxiliary task can help CNN-functions that estimate properties in world coordinates.

Student Background

Master studies, experience with Machine Learning and Computer vision. For example, the Signal- and image processing profile from the D- and Y-programs.

Thesis work is suitable for 1-2 students.

Location: Linköping & Stockholm

*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.

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