* Fixed the build process for MacOS X.
* Re-activeted vigra-config (script to query VIGRA installation information)
and added VigraConfig.cmake (query VIGRA installation information from within
cmake).
* Added CDash support (nightly builds and tests).
* Added convexHull().
* Added vigra::Box.
* Added vigra::Sampler class to sample given data in various ways.
* Added much new functionality to the vigra::RandomForest class (e.g. more
split strategies, variable importance measures, feature selection)
* Added readSIF() (reader for the Andor SIF file format).
* Added vigra::HDF5File for easier navigation in HDF5 files.
* Added recursive approximation of the Gaussian filter
(recursiveGaussianFilterX(), recursiveGaussianFilterY())
* vigranumpy: added Gabor filtering.
* Fixed multi-threading bugs at various places.
* Minor improvements and bug fixes in the code and documentation.
VIGRA is a computer vision library that puts its main emphasize on
flexible algorithms, because algorithms represent the principle know-how
of this field. The library was consequently built using generic
programming as introduced by Stepanov and Musser and exemplified in the
C++ Standard Template Library. By writing a few adapters (image iterators
and accessors) you can use VIGRA's algorithms on top of your data
structures, within your environment. Alternatively, you can also use the
data structures provided within VIGRA, which can be easily adapted to a
wide range of applications. VIGRA's flexibility comes almost for free:
Since the design uses compile-time polymorphism (templates), performance
of the compiled program approaches that of a traditional, hand tuned,
inflexible, solution.
