Modulbeschreibung

• Students know how images and 3D data are represented and manipulated by software.
• Students know the most important problems related to image analysis: e.g. image classification, segmentation and object detection and tracking.
• Students can apply machine learning and deep learning techniques to solve image-related problems, and deal with practical issues arising in the field (dataset engineering, data augmentation, data normalization).
• Students have seen different examples of image analysis problems and common solution techniques, and are able to acquire additional expert knowledge from the scientific literature and online resources.

Basic image processing methods applied to document processing:

• applications to OCR: handcrafted features; convolutional neural networks.
• Image classification with small datasets: data augmentation techniques;

Segmentation

• applications to medical images (2D, 3D)
• fully convolutional networks for semantic segmentation.

Object detection

• face and pedestrian detection with cascading classifiers
• object tracking in videos.

Generative models and Image Synthesis

• Applications to Image Inpainting

Evaluieren und Anwenden von Algorithmen zur Segmentierung und Registrierung von Punktwolken, sowie Objekt Detektion und Klassifizierung.

Verarbeitung von 3D-Punktwolken mit traditionellen Methoden sowie Techniken des Machine Learning