Stackoverflow Research Papers

Posted on by Turg

The answer to your question is actually pretty simple.

There is no mention of any research paper because no one mentioned it. There is no obligation to refer to papers.

OpenCV is open source. That's why it's named OpenCV If you want to know the "internal implementation of OpenCV StereoBM" I suggest you simply take a look into the implementation.

The also mention the contributor of that algorithm. Kurt Konolige. So I suggest you ask that guy why he did not mention any papers in his documentation. Or maybe check out his papers and see if you find anything related. Maybe there isn't a paper at all.

The documentation mentions that it is using the block-matching algorithm though and there is definitely more than enough literature available online.

answered Feb 27 at 14:47


An Analysis of the Stack Overflow Q&A Site

This project analyzes a Question & Answer site for programmers, Stack Overflow, that dramatically improves on the utility and performance of Q&A systems for technical domains. Over 92% of Stack Overflow questions about expert topics are answered — in a median time of 11 minutes. Using a mixed methods approach that combines statistical data analysis with user interviews, we seek to understand this success. We argue that it is not primarily due to an a priori superior technical design, but also to the high visibility and daily involvement of the design team within the community they serve. This model of continued community leadership presents challenges to both CSCW systems research as well as to attempts to apply the Stack Overflow model to other specialized knowledge domains..

This project is complete and no longer under active development.


Our analysis is based on the August 2010 Stack Exchange Data Dump (creative-commons licensed). We analyzed two years of user activity — from July 31, 2008 to July 31, 2010. As of early August 2010, Stack Overflow had a total of 300k registered users who asked 833k questions, provided 2,2M answers, and posted 2,9M comments.

Source Code

Our analysis code is available for download under a BSD license: (40mb)

We converted the XML data dump files into a SQLite3 database. Analysis code is written in Python 2.x and SQL. Graphs are generated using matplotlib. This file is large because it contains some intermediate results of large queries.

Build Instructions

  1. Download the source file above and unzip into a directory of your choice.
  2. Download the data dump and place XML files into directory xml/ - see xml/README.txt.
  3. Run the import script import/ (it calls python scripts to import individual tables)
  4. Create indices to speed up queries using import/create-indices.sql.
  5. Individual analysis scripts (both in Python and SQL) are in folder analysis/
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