Masterstudium Visual Computing - Fakultät für Informatik, TU Wien

• The students are aware of the theories behind digital media, human perception,
semantics and concepts.
• The students are able to identify the fundamental building blocks of feature transforms.
• The students are able to extract simple color, texture, shape, audio and video
features from audiovisual content.
• The students are able to extract complex local features, spectral audio features
and time-based video features from audiovisual content.
• The students are able to extract semantic features from media content.
• The students are able to apply statistical ltering methods on the extracted features.
• The students are aware of dynamic ltering methods.
• The students are able to select and apply a simple classication method on the
extracted features.
• The students understand the four psychological theories of human similarity perception
and are able to apply them.
• The students understand complex machine learning techniques such as Support
Vector Machines and Mixture Models.
• The students understand brain-like articial neural networks such as Spiking nets,
Hopeld nets and Boltzmann machines.
Kognitive und praktische Fertigkeiten:
• The students are aware of the psychological theory of human similarity perception.
• The students are able to implement media understanding applications in JavaME
on mobile devices.
• The students are able to implement sophisticated media understanding applications
in Matlab.
Soziale Kompetenzen, Innovationskompetenz und Kreativität:
• The students gain experience in scientic team work
• The students are able to discuss the lecture contents in scientic discourses.
• The students are able to discuss semantic features extraction and classication in
scientic discourses.
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