The students understand the essential mechanisms for generating images, are able to compare different camera systems and can apply them depending on the situation.
They are able to optimize image recording by cameras with suitable lighting. They know the essential bus systems for transporting images and can use them according to their properties.
The students master the basic methods of image processing, such as lookup tables, filters in the spatial and frequency domain, and morphological operations.
They are also able to apply methods of image analysis, such as pattern recognition or measurements in images.
The students can apply the already known procedures and methods of artificial intelligence (module "AI in Systems Design") to image data.
They are able to describe and classify images using descriptors.
They can forward the results of the image analysis to higher-level systems in machine-readable form.

None

The module covers the following topics/contents:
Part 1: Basics of image generation and image transport

• Camera systems and other methods of image generation
• General conditions of generation (lighting, etc.)
• Bus systems and interfacesC
• Compression and decompression methods

• Image processing libraries and APIs
• Lookup tables
• Filters in the spatial and frequency domain
• Morphological features
• Methods of image analysis

• Image descriptors
• Image Classification and Machine Learning
• Deep learning in computer vision

• A. Rosebrock, Deep Learning for Computer Vision with Python, PyImageSearch, 2017
• R. Szeliski, Computer Vision: Algorithms and Applications, 2nd ed., Springer, 2022
• R. C. Gonzalez, R. E. Woods, Digital Image Processing, 4th ed., Pearson, 2017
• J. Howse, J. Minichino, Learning OpenCV 4 Computer Vision with Python 3, 3rd ed., Packt Publishing, 2020

Lectures, joint programming exercises, programming homework
Group size: max. 20

Integrated module examination
Immanent examination character: Assessment of the programming homework, written and practical final examination.