IAM

DAVIDSTUTZ

I am looking for full-time (applied) research opportunities in industry, involving (trustworthy and robust) machine learning or (3D) computer vision, starting early 2022. Check out my CV and get in touch on LinkedIn!

TAG»OPENCV«

ARTICLE

Compiling OpenCV 2.4.x with CUDA 9

Currently, both OpenCV 2 and OpenCV 3 seem to have some minor issues with CUDA 9. However, CUDA 9 is required for the latest generation of NVidia graphics cards. In this article, based on this StackOverflow question, I want to discuss a very simple patch to get OpenCV 2 running with CUDA 9.

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29thJANUARY2017

PROJECT

Revised C++ implementations of two popular superpixel algorithms, SEEDS and FH, which are shown to outperform the original implementations.

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05thDECEMBER2016

PROJECT

A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.

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ARTICLE

Running OpenCV and Google GLog with CMake on Travis CI

Running C++ projects on Travis CI may be challenging depending on the libraries used. Here I briefly describe how to run OpenCV and Google GLog on Travis CI.

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07thJUNE2016

PROJECT

Efficient C++ implementation for hierarchical graph-based image segmentation.

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09thMAY2016

PROJECT

Efficient C++ implementation of iPiano, a proximal algorithm with inertial force for non-convex and non-smooth optimization; including applications to image segmentation.

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ARTICLE

Fast, Multi-Label Connected Components in MatLab

This article presents a MatLab MEX wrapper for a fast, multi-label connected components implementation in C++ originally written by Ali Rahimi.

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ARTICLE

Optical Flow I/O with OpenCV

This article presents an OpenCV wrapper for the Flow I/O code provided by the Sintel dataset [2].

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ARTICLE

Efficient Hierarchical Graph-Based Video Segmentation: An Implementation

This article presents an efficient implementation of the hierarchical graph-based video segmentation algorithm proposed by Grundmann et al. [1]. The implementation is available on GitHub.

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ARTICLE

Efficient High-Quality Superpixels: SEEDS Revised

SEEDS Revised is a new implementation of the superpixel algorithm SEEDS [1] used for evaluation in my bachelor thesis “Superpixel Segmentation using Depth Information”. This article introduces the basic concepts of SEEDS as well as the usage of SEEDS Revised.

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