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Course Material for the course Visual Computing (VCO2-IM)

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Visual Computing

Repository for the Visual Computing course (VCO2-IM.ma VZ SS24) at the FH Hagenberg.

Contact: David C. Schedl.

Tutorials:

# Tutorial (link to .ipynb) Open in Colab
1 Python Tutorial Open In Colab
2 OpenCV Tutorial Open In Colab
3 Filters Open In Colab
4 Edges and Lines Open In Colab
5 Corners and Featuers Open In Colab
6 Alignment Open In Colab
7 Stereo Open In Colab
8 (recap) Neural Networks Open In Colab
9a CNN (LeNet in TensorFlow) Open In Colab
9b CNN (LeNet in PyTorch) Open In Colab
10 Transfer Learning a CNN Open In Colab
11 Image representation with an MLP Open In Colab

Homework Tasks:

# Homework (link to .ipynb) Open in Colab
1 Compression Open In Colab
2 ML Compression Open In Colab

Python Setup:

Students have the option to run the code online with Google Colab (requires a Google account) or locally with a local installation of Python.

Online:

Everything runs on a Google machine, so you don't need to set up anything on your computer. Furthermore, the machines come with the most popular libraries preinstalled. Just click on the corresponding Open in Colab badge: Open In Colab.

Local:

Install Python on your computer via Conda/Miniconda or the Python Installer. Use Python3, as Python2 is not supported anymore. Furthermore, you need an Editor that supports Jupyter (.ipynb) notebooks. I recommend using Visual Studio Code. Optionally, you can also use a local server and open Notebooks in your browser (Visual Studio simplifies this).

Useful Links:

Course Grading:

This course will be graded based on your performance in the course homeworks. The homework tasks will be announced while we progress through the course.

Furthermore, you will give an oral presentation about a related computer vision field. The topic of your presentation is free to choose (as long as it is CV related). Focus on a Computer-Vision task/algorithm (a scientific work), understand its details (advantages, limitations, competition, ...), and explain it to your fellow students.

IMPORTANT: Pick a fun topic that interests you! You can find further details here and an inspiration for topics here.

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Course Material for the course Visual Computing (VCO2-IM)

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