SmartVHB

With the support of Virtuelle Hochschule Bayern, we have created a repository of blended learning units in the area of Computer Vision. They are intended to help students focus on specific learning objectives within 45 minutes of self study and can be integrated free of charge by teachers at all Bavarian University in their curriculum.

If you are a teacher or student enrolled at a Bavarian university, you can access the units here: smart.vhb.org.

For selected units, there is also accompanying source code. For users outside (and inside) of Bavaria, we make this code available as Jupiter Notebooks on mybinder.org and gitlab.com.

The units (partly in German, partly in English) encompass the following topics:

• Absolute Orientation, Aligned Rank Transform, Analysis of Variance, Analysis of Variance with Repeated Measures
• Bildgebung mittels Perspektivischer Projektion, Bounding Volume Hierarchies for Raytracing
• Chi-Squared Tests, Cholesky-decomposition, Contrasts and Multiple Comparisons
• Eigenwerte und Eigenvektoren, Einführung in Bayessche Optimierung, Einführung in die Lineare Ausgleichsrechnung, Einführung in Nichtlineare Ausgleichsrechnung für Computer Vision, Einführung in Structure-from-Motion, Epipolargeometrie, Equivalence Testing, Fitts' Law for Multiple Dimensions
• Friedman Test
• Gauss-Newton Verfahren, Givens-Rotations, Goals Operators Methods and Selection Rules, Gradientenabstiegsverfahren, Gram-Schmidt Orthogonalization Process
• Hauptkomponentenanalyse, Head Tracking for Immersive Data Glasses, Hick's Law, Hierarchical Task Analysis, Householder Transformation
• Interaction Styles in Human Computer Interaction, Introduction to Convolutional Neural Networks for Computer Vision, Introduction to Digital Sound Effect Design, Introduction to Evaluation Methods for Human-Computer Interaction, Introduction to Eye-tracking, Introduction to Fitts's Law, Introduction to Generative Adversarial Networks, Introduction to Interaction Techniques for 3D User Interfaces, Introduction to Markov Models, Introduction to Model Evaluation Techniques for Machine Learning, Introduction to Object Detection, Introduction to Probability Distributions, Introduction to Probability Theory, Introduction to Sampling Algorithms, Introduction to Segmentation, Introduction to Spatial Acceleration Data Structures for Raytracing, Introduction to the Numerical Computation of Singular Value Decomposition, Iterative Closest Point
• Kamerakalibrierung mittels Direkter Linearer Transformation, Keystroke-Level-Model, Korrelation und Faltung mittels Linearen Bildfiltern, Kruskal-Wallis Test
• Levenberg-Marquardt Verfahren, LU-Decomposition
• Mann-Whitney-U-Test, Markerbasierte Posenbestimmung, Markov-Chain-Monte-Carlo-Process, Mental Models in Human-, Computer Interaction, Merkmalsbeschreibung, Merkmalsdetektion, Merkmalszuordnung
• Optical-See-Through Kalibrierung, Optischer Fluss
• Parametrisierung von 3D Rotationen, Perspective-N-Point, Projektor-Kamera-Kalibrierung
• QR Decomposition
• Random Sample Consensus
• Singulärwertzerlegung, Steering Law, Stereokorrespondenzen, Stereoscopic Image Synthesis for Immersive Data Glasses, Student's t-Test
• The k-d-Tree for Raytracing , Triangulierung, Types of Power Analysis
• User Research Methods and Analysis Techniques
• Which Statistical Test?!, Wilcoxon Signed-Rank Test