For the lecture “Computer Vision”, I had to solve each week a new task to hand in. All the tasks had to be solved with Matlab, and a report had to be written with the conclusion about the implementation, advantages, disadvantages, and problem cases of the implemented algorithms.
- Create a feature detector/descriptor to find matching features between two images. The first task was to implement the Harris corner detector, and in a later step, SIFT features had to be extracted.
- Implementation of the “Direct Linear Transform algorithm” and the “Gold Standard algorithm” to calibrate the camera with intrinsic parameters and to estimate the distortion coefficients of the lens. As we have then the relative position of the camera, we can render objects with the correct transformation on the picture.
- Implementation of the “Monte Carlo Localization using Particle Filter” to help a robot to localize himself in a 2D corridor.
- Estimation of the Fundamental Matrix, Essential Matrix, and Camera Matrix with the help of the RANSAC.
- Reconstruction of a 3D object from multiple calibrated images with a naive silhouette extraction algorithm.
- Image segmentation with the “Mean-Shift Segmentation” and the “EM Segmentation” (Expectation-Maximization).
- Triangulation based on multiple images from an object with different angles to create the structure/model.
- Building a condensation tracker which is based on color histograms. This was applied to various videos with different objects, from which one has been tracked.
- Image Categorization with a bag-of-words image representation.