|
Learning target / Competences
|
Target skills:
The student will gain an overview on established and modern image processing techniques. The course provides tools, methods, models and techniques for the following topics: image formation, optics, imagers, color, image segmentation, image analysis, image features, image alignment, estimation in computer vision, programming and deep learning.
Competences:
The student will understand basic problems in image processing and machine vision, e.g. image segmentation, feature detection, image matching or estimation problems in alignment.
He/she will know methods, algorithms and common techniques to solve the above mentioned problems. The student will be able to computationally apply the methods on
given low-level and higher-level image processing tasks in real world computer vision problems.
|
|
Lectures
|
Maschinelles Sehen mit Labor
| Type |
Lecture/lab |
| Nr. |
EMI2247 |
| Hours per week |
4.0 |
| Content |
Lecture contents:
Feature-based methods:
- Feature detectors and feature descriptors
- SIFT detector and descriptor
Image Transformations:
- Affine and Projective Transformations
- Robust transformation estimation (RANSAC)
Image Motion and Tracking
- Visual odometry and optical flow (local and global methods)
Machine learning in image processing
- Clustering/Segmentation: k-means, SLIC Superpixel, spectral methods
- Classification: Support Vector Machines
Deep learning in machine vision
- Fundamentals of deep neural networks in image processing (convolutional neural networks, CNNs)
- Training and training data collection
- Object classification with neural networks
- Object detection and segmentation with neural networks
Laboratory contents:
- image mosaicing: image transformations and scale-invariant feature detectors
- Visual Odometry: Non-contact speed determination in video sequences
- Machine learning methods for segmentation: K-Means in image compression
- Deep Learning: Object classification and detection
- Deep Learning: Keras, Tensorflow and python-based open source usage
Literature:
- Szeliski, R., Computer Vision: Algorithms and Applications; Springer, 2011, online pdf version: http://szeliski.org/Book/
- Burger, Burge, Digital Image Processing - An algorithmic introduction, 3rd ed. Springer, 2015
- Gonzalez, Digital Image Processing, 4th ed., Pearson, 2017
- Goodfellow, Bengio, Courville, Deep Learning, MIT Press 2016, onlineversion: http://www.deeplearningbook.org/
|
| Literature |
- Szeliski, R., Computer Vision: Algorithms and Applications; Springer, 2011, online pdf version: http://szeliski.org/Book/
- Burger, Burge, Digital Image Processing - An algorithmic introduction, 3rd ed. Springer, 2015
- Gonzalez, Digital Image Processing, 4th ed., Pearson, 2017
- Goodfellow, Bengio, Courville, Deep Learning, MIT Press 2016, onlineversion: http://www.deeplearningbook.org/
|
|