After successful completion of the course, students are able to name, explain and contrast the most important theories, principles, concepts and algorithms of computer vision. Their knowledge and understanding corresponds to the state-of-the-art literature in the field of computer vision.
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)
ECTS breakdown (estimate, recommendation):
28 hrs. lecture
54 hrs. preparation units, individual preparation and tests
68 hrs. solving exercises and hand-in meetings
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150 hrs. corresponds to 6 ECTS at 25 hrs. each.
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.
Python knowledge advantageous, but not required.
