Thesis: Synthetic Images for Machine Vision and

Thesis: Synthetic images for machine vision and image processing*
- SICK AG- Waldkirch (bei Freiburg), DE, 79183- Full-time**Winter semester 2024/25 - 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.