1300282207
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Please find all information here:
https://hci.iwr.uni-heidelberg.de/content/computer-vision
*** Formalities:
Teaching assistants (main point of contact): Friedrich Feiden: johann-friedrich.feiden@iwr.uni-heidelberg.de; Zavadski, Denis: denis.zavadski@iwr.uni-heidelberg.de
Registration: Moodle: https://moodle.uni-heidelberg.de/course/view.php?id=24146
Prerequisite: no prerequisites, but it is recommended to have Machine Learning Background, e.g. Fundamentals of Machine Learning or equivalent
Exam: Either oral exam or mini-project (as in last years).
Leistungspunkte: 6 LP
Usability: Physics, MSc., Angewandte Informatik, MSc. Scientific Computing
Lehrziel
- Brief Introduction to necessary Machine Learning (incl. U-Net, ResNet, Vision Transformers) - Basic Image Processing (incl. linear/non-linear Filtering) - Sparse feature Detection and Description (incl. SIFT and LIFT) - Projective Geometry, Epipolar Geometry - Sparse 3D Reconstruction , SLAM and Camera Localization - Neural Randiance Fields (NERF) - Robust Matching (incl. Differentiable RANSAC) - Object Tracking (incl. Particle Filter, Kalman Filter, ) - Object Recognition - Image Generation (incl. GAN, Diffusion, VAE, Flows) - Training Data Generation
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