Course description
The lecture will cover advanced computer vision methods in depth:
• Texture, Scenes, und Context
• Local- and Multiscale Representations
• Interest Points, Corners
• Scene Emergent Features
• Scene Recognition, Bag of Words, SIFT
• Clustering, Pyramid Matching, Support Vector Machine
• Deep Learning, CNNs
• Perceptron, Linear Basis Function Models, RBF
• Neural Networks architectures und learning methods
• Error functions and methods for parameter optimization (e.g., pseudo-inverse,
gradient descent, Newton method)
• Duality, Sparsity, Support Vector Machine
• Unsupervised methods and Self-Organizing Maps (SOM)
Prerequisites
Mathematics: vector and matrix calculus, from linear algebra
Programming, object-oriented programming
Computer Vision knowledge, from module Introduction to Visual Computing (both parts) and its prerequisite modules.
For the exercise part, students must be able to program in Python 3.
Grading
There will be a written exam that covers the lecture part (50% of the grade). The exercise part is also worth 50% of the grade.
Exam
Written exam at the end of the semester as well as assignment interviews for the exercise.