Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:
- 12-15 ECTS in technical scientific modules (TSM)
TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
- 9-12 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
- 6-9 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.
In the module description (download pdf) you find the entire language information per module divided into the following categories:
Analyzing images is a very complex task that has many important real-world applications. This module presents powerful techniques to extract information from images and 3D data, based on machine learning and deep learning methods. These methods are mostly used as “black boxes” and their inner workings are not discussed in much detail. The module provides an overview of many image analysis applications such as document analysis, medical imaging and autonomous driving; examples of advanced uses of deep learning on images (generative networks for image synthesis, adversarial networks, neural style transfer) are also discussed.
- 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
- 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
Contents of Module
- Basic image processing methods applied to document processing: binarization; 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: data augmentation techniques; one-shot learning; transfer learning and pre-trained models.
- applications to medical images (2D, 3D)
- fully convolutional networks for semantic segmentation.
- Object detection
- face detection with cascading classifiers
- pedestrian detection for autonomous driving
- object tracking in videos.
- Generative models and Image Synthesis
- Applications to Image Inpainting;
- Generative Adversarial Networks;
- Neural style transfer.
Teaching and Learning Methods
Classroom teaching; programming exercises using python and frameworks in python
- Computer Vision: Algorithms and Applications, Richard Szeliski, 2010
- Deep Learning with Python, Francois Chollet, early 2018, Sections 5, 8.3, 8.5