Home > Computer Vision > Computer Vision: IM520 Class Report (fall 2011/summer 2012)

## Computer Vision: IM520 Class Report (fall 2011/summer 2012)

This short post deals with contents, assignments and solutions of the (very cool) IM520 computer vision class by FH-Prof. DI Dr. Wilhelm Burger at the Campus Hagenberg of the University of Applied Sciences Upper Austria, which I attended from fall 2011 to summer 2012. I’ve delayed this post for quite a while now – so that newer IM520 assignments had a chance to (at least partially) change over time. The core part of this post is making the class report public that each of us was required to submit – which contains details on how I solved all assignments, including problem descriptions, solution ideas, source snippets and lots of figures.

• My full IM520 class report can be found here (24MB).
• Assignment instructions (1-12) can be found here.
• Files additionally required besides the instructions to start assignments be found here (30MB).
• Currently, I’m providing the implementation source code only on individual request.

To make it easier for you to see if the report contains your topic of interest, here’s the table of content:

Preface

1 Introduction
1.1 Assignment 1.1: Setting up the development environment
1.2 Assignment 1.2: Testing the development environment
1.3 Assignment 1.3: Building a graph
1.4 Assignment 1.4: Mark branch nodes in graph
1.5 Assignment 1.5: Graph Cleanup

2 Fitting Lines to Points
2.1 Assignment 2.1: Implementing Algebraic Lines
2.2 Assignment 2.2: Fitting Lines to Point Sets

3 Finding Edges and Corners

4 Correlation and Log-Polar Transformation
4.1 Assignment 4.1: Correlation-based matching in gray scale images
4.2 Assignment 4.2: Log-Polar Transform

5 Texture Clustering
5.1 Assignment 5.1: Law’s texture maps
5.2 Assignment 5.2: K-means feature clustering

6 Skin Detection based on Colors 33

7 Motion Detection
7.1 Assignment 7.1: Frame-Differencing Method
7.2 Assignment 7.2: Running-Average Method
7.3 Assignment 7.3: Running-Gaussian-Average Method
7.4 Comparison of the Approaches
7.5 Assignment 7.4: Selective Background Updating

8 Tracking: Greedy Exchange Algorithm

9 Human Motion Detection based on SIFT

10 3D Camera Calibration using the Zhang Approach
10.1 Assignment 10.1: Focal Length Calculation
10.2 Assignment 10.2: Rotation Matrix Orthonormality Verification
10.3 Assignment 10.3: Scene Point Projection

Listings

Bibliography

Categories: Computer Vision