Industrial Image Processing (E)

Differential and integral calculus, linear algebra

• Systems for capturing digital images (2D and 3D)

• Classical basics of image processing (filtering, segmentation, feature recognition)

• Camera calibration, stereo analysis

• Deep learning-based models (for image classification, object recognition, segmentation)

Students will be able to

• evaluate camera systems and apply them for capturing image data (2D and 3D)
• use classical image analysis methods to solve problems
• use 3D image information for industrial applications
• evaluate and deploy deep learning-based models

• Lectures
• Exercises with Matlab/Python with feedback
  • partly based on contents of research articles
  • asynchronously accompanied

Written exam

None

• Gonzalez, R., & Woods, R. (2017). Digital Image Processing, Global Edition. Harlow, United Kingdom: Pearson Education, Limited.
• Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge, United Kingdom: Cambridge University Press.
• Szeliski, R. (2022). Computer Vision: Algorithms and Applications. Cham, Switzerland: Springer International Publishing AG.
• Burger, W., & Burge, M. J. (2022). Digital Image Processing: An Algorithmic Introduction. Cham, Switzerland: Springer International Publishing AG.
• Goodfellow, I.; Bengio, Y.; Courville, A. (2016): Deep Learning. MIT Press. Online im Internet: URL: https://www.deeplearningbook.org/
• Norvig, P.; Russel, S. (2021): Artificial Intelligence: A Modern Approach, Global Edition.

Presence

Accompanied asynchronously