Currently it is difficult to successfully link C++ projects with Tensorflow. However, to compile and run smaller code snippets based on Tensorflow, it might be convenient to put the code inside the tensorflow code base and compile an individual executable using Bazel.
In this article, I discuss a simple Tensorflow operation implemented in C++. While the example mostly builds upon the official documentation, it includes trainable parameters and the gradient computation is implemented in C++, as well. As such, the example is slightly more complex compared to the simple ZeroOut operation discussed in the documentation.
Revised C++ implementations of two popular superpixel algorithms, SEEDS and FH, which are shown to outperform the original implementations.
This article presents an implementation of Felzenszwalb and Huttenlocher’s [1] graph-based image segmentation algorithm. The implementation is compared to the original implementation by Felzenszwalb in terms of Boundary Recall, Undersegmentation Error and Explained Variation, as used for evaluating superpixel algorithms. In addition, qualitative results are provided. The implementation is publicly available on GitHub.
A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.
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.
Efficient C++ implementation for hierarchical graph-based image segmentation.
Efficient C++ implementation of iPiano, a proximal algorithm with inertial force for non-convex and non-smooth optimization; including applications to image segmentation.
There are many articles on preparing for coding interviews; this is my personal reading list for preparing for a Microsoft Interview (from a C++ perspective).