Several mathematical impage processing exercises implemented in C++ and MatLab.
The Berkeley Segmentation Benchmark extended by superpixel metrics.
Tools to pre-process the NYU Depth v2 segmentations for evaluation.
A two-layer perceptron for digit classification on MNIST implemented in MatLab.
A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.
This article presents a MatLab MEX wrapper for a fast, multi-label connected components implementation in C++ originally written by Ali Rahimi.
A comparison of several state-of-the-art superpixel algorithms using an extended version of the Berkeley Segmentation Benchmark.
In the course of my seminar paper on neural networks and their usage in pattern recognition I came across the MNIST dataset. The MNIST dataset provides test and validation images of handwritten digits. As result, I implemented a two-layer perceptron in MatLab to apply my knowledge of neural networks to the problem of recognizing handwritten digits.
During my fifth semester at RWTH Aachen University I participated in a seminar offered by the Chair of Computer Science 6 on “Selected Topics in Human Language Technology and Pattern Recognition”. As result, I wrote a seminar paper on neural networks for pattern recognition titled “Introduction to Neural Networks”. At the end of the seminar, all participants had to give a short talk on their topic. Both my seminar paper and my slides of the talk can be found here.