GSoC 2014 – Signing off

This years GSoC coding period has nearly come to an end. This post aims to briefly summarize everything that happened during the last three months. My task was to implement Region Adjacency Graph based segmentation algorithms for scikit-image. This post provides a good explanation about them. Below I will list out my major contributions.

Contributions

Region Adjacency Graphs

Fixing the API for RAGs was very important, since it was directly going to affect everything else that followed. After a long discussion and some benchmarks we finally decided to have NetworkX as a dependency. This helped a lot, since I had a lot of graph algorithms already implemented for me. The file rag.py implements the RAG class and the RAG construction methods. I also implemented threshold_cut, a function which segments images by simply thresholding edge weights. To know more, you can visit, RAG Introduction.

Normalized Cut

The function cut_normazlied, implements the Normalized Cut algorithm for RAGs. You can visit Normalized Cut on RAGs to know more. See the videos at the end to get a quick idea of how NCut works. Also see, A closer look at NCut, where I have benchmarked the function and indicated bottlenecks.

Drawing Regions Adjacency Graphs

In my posts, I had been using a small piece of code I had written to display RAGs. This Pull Request implements the same functionality for scikit-image. This would be immensely useful for anyone who is experimenting with RAGs. For a more detailed explanation, check out Drawing RAGs.

Hierarchical Merging of Region Adjacency Graphs

This Pull Request implements a simple form of Hierarchical Merging. For more details, see Hierarchical Merging of Region Adjacency Graphs. This post also contains videos at the end, do check them out. This can also be easily extended to a boundary map based approach, which I plan to do post-GSoC

 

Final Comments

The most important thing for me is that I am a better Python programmer as compared to what I was before GSoC began this year. I was able to see how some graph based segmentation methods work at their most basic level. Although GSoC has come to an end, I don’t think my contributions to scikit-image have. Contributing to it has been a tremendous learning experience and plan to continue doing so. I have been been fascinated with Image Processing since me and my friends wrote an unholy piece of Matlab code about 3 years ago to achieve this. And as far as I can see its a fascination I will have for the rest of my life.

Finally, I would like to thank my mentors Juan, Johannes Schönberger and Guillaume Gay. I would also like to thank Stefan for reviewing my Pull Requests.

 

 

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