Jedes Modul umfasst 3 ECTS. Sie wählen insgesamt 10 Module/30 ECTS in den folgenden Modulkategorien:
- 12-15 ECTS in Technisch-wissenschaftlichen Modulen (TSM)
TSM-Module vermitteln Ihnen profilspezifische Fachkompetenz und ergänzen die dezentralen Vertiefungsmodule. - 9-12 ECTS in Erweiterten theoretischen Grundlagen (FTP)
FTP-Module behandeln theoretische Grundlagen wie die höhere Mathematik, Physik, Informationstheorie, Chemie usw. Sie erweitern Ihre abstrakte, wissenschaftliche Tiefe und tragen dazu bei, den für die Innovation wichtigen Bogen zwischen Abstraktion und Anwendung spannen zu können. - 6-9 ECTS in Kontextmodulen (CM)
CM-Module vermitteln Ihnen Zusatzkompetenzen aus Bereichen wie Technologiemanagement, Betriebswirtschaft, Kommunikation, Projektmanagement, Patentrecht, Vertragsrecht usw.
In der Modulbeschreibung (siehe: Herunterladen der vollständigen Modulbeschreibung) finden Sie die kompletten Sprachangaben je Modul, unterteilt in die folgenden Kategorien:
- Unterricht
- Dokumentation
- Prüfung
Analyzing images is a complex task that has many important real-world applications. In this module, we first present some foundations of image processing, such as filters, binarization, edge detection and finding lines and objects. We then study methods based on machine learning and deep learning to classify images, detect and localize objects and segment images pixelwise for example for medical image analysis. The most important deep learning architectures are discussed as well as some advanced uses for image synthesis, such as adversarial networks and neural style transfer.
Eintrittskompetenzen
Prerequisites:
- Basic knowledge of machine learning (e.g. Andrew Ng’s ML course on Coursera)
- Good command of an imperative programming language, basic knowledge of Python (the module will use Python 3).
- www.scipy-lectures.org/index.html Sections 1.1, 1.2, 1.3, 3.6.1, 3.6.2
- Basic knowledge of probability, statistics, linear algebra (vectors, matrices)
- Students are expected to take their laptops for the Lab activities
Lernziele
- 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 localisation
- 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
Modulinhalt
- Introduction
- Basic image processing methods applied to document processing: binarization; edge detection, filtering, segmentation of text into lines, words and characters; connected component analysis.
- Image classification
- applications to OCR: handcrafted features; convolutional neural networks.
- Image classification with small datasets.
- Segmentation
- applications to medical images (2D, 3D)
- fully convolutional networks for semantic segmentation.
- Object detection
- face detection with cascading classifiers
- pedestrian detection for autonomous driving
- 2 stage and single shot approaches for object detection and localisation
- Generative models and Image Synthesis
- Applications to Image Inpainting;
- Generative Adversarial Networks;
- Neural style transfer.
Lehr- und Lernmethoden
Classroom teaching; programming exercises using python and frameworks in python
Bibliografie
- Computer Vision: Algorithms and Applications, Richard Szeliski, 2010
- Deep Learning with Python, Francois Chollet, early 2018, Sections 5, 8.3, 8.5
Vollständige Modulbeschreibung herunterladen
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