Thesis: Synthetic Images for Machine Vision and - Waldkirch, Deutschland - SICK AG

SICK AG

Geprüftes Unternehmen

Waldkirch, Deutschland

vor 4 Wochen

Geschrieben von:

Lena Wagner

beBee Recruiter

Beschreibung

Thesis:
Synthetic images for machine vision and image processing*- SICK AG- Waldkirch (bei Freiburg), DE, Full-time
Winter semester 2024/2- limited to 3-6 months

Synthetic images for machine vision and image processing:

In the past decades, camera systems with increasingly sophisticated capabilities have become ubiquitous. Ranging from cameras embedded in our smartphones to camera arrays in self-driving cars and camera systems controlling industrial manufacturing process, these systems are revolutionizing industries and our everyday lives. At the heart of all these imaging systems lies the image acquisition process with the aim to achieve a "good" input image. Therefore, machine vision engineers often spend a lot of time and go through some empirical best practices to try and evaluate different geometrical and optical configurations of the imaging system. To speed up this tedious task, realistic synthetic images allow to evaluate, analyze, and optimize imaging setups without employing physical parts and sensors. To this end sensor-realistic synthetic images are needed, including noise and defects.
The goal in this thesis will be to setup a simulation to produce realistic synthetic images, starting with a virtual environment, simulating the imaging process, and then processing the images computationally.

YOUR TASKS:

You compare different simulation tool chains
You set up a tool chain to generate realistic synthetic images
You evaluate different detection task based on synthetic images

YOUR PROFILE:

You are studying Computer Science, Computer Vision, Machine Learning or a related field
Basic knowledge in optics and physics
Programming skills in e.g. Python, Matlab
You are a team player
Your independent and creative working style rounds off your profile

YOUR APPLICATION:

Sarah Disch
Job-ID
35861:
At SICK, we see people, not gender.
We put great emphasis on diversity, reject discrimination and do not think in categories such as gender, ethnicity, religion, disability, age or sexual identity.