Project Euler Profile:
SXJ: A gtalk-bot demo.
Shortest path: a C++ meta-programming implementation of Bellman shortest path.
Libraries for Computer Vision Research
I am now preferring C++, template and generic programming.
So I recommend the following libraries for computer vision research:
- Basic data structures and algorithms: Boost
(not the boosting in computer vision). Boost has a large collection of very useful and elegant
organized data structures and algorithms. Among them, I am familar with:
Computer vision algorithms, there are several options
Visualization, which is very important for computer vision algorithm debugging
I am now use the combination: ublas+OpenCV+plplot, which can collaborate to mimic the Matlab functionality.
- ublas --- an nearly perfect wrapper of uBLAS library for linear algebra.
- string_algo --- good supplement for STL string algorithms.
- multi_array --- versatile any dimensional array.
- random --- random number generator.
- graph --- abstract and flexiable graph data structure.
I hosted a project call boostcvpr on sourceforge,
implemented a collection of computer vision algorithms using boost data structure.
I once participated in scl, an image processing library.
I am familar with these tools.
- VC (6, 7, 8)
- GCC, make,
real C++ programming
- CMake, cross platform making
- This website is developed in and hosted by Google Appengine.
165959 visistors since August 2009. (C) Haifeng GONG, 2008-2012.