Computer vision Library
VIGRA stands for "Vision with Generic Algorithms". It is a novel
computer vision library that puts its main emphasis on customizable
algorithms and data structures. By using template techniques similar to
those in the C++ Standard Template Library, you can easily adapt any
VIGRA component to the needs of your application, without giving up
execution speed.
- Developed at multimedia:libs
- Sources inherited from project openSUSE:Factory
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Source Files
| Filename | Size | Changed |
|---|---|---|
| baselibs.conf | 0000000016 16 Bytes | |
| vigra-1.11.0-src.tar.gz | 0051854568 49.5 MB | |
| vigra.changes | 0000006301 6.15 KB | |
| vigra.spec | 0000003762 3.67 KB |
Revision 30 (latest revision is 38)
Dominique Leuenberger (dimstar_suse)
accepted
request 444703
from
Takashi Iwai (tiwai)
(revision 30)
- Version update to 1.11.0:
see http://ukoethe.github.io/vigra/doc-release/vigra/CreditsChangelog.html
* Ported vigranumpy to Python 3.5.
* Added Chunked arrays to store data larger than RAM as a collection
of rectangular blocks.
* Added vigra::ThreadPool and parallel_foreach() for portable
algorithm parallelization based on std::thread.
* Implemented parallel versions of Gaussian smoothing, Gaussian
derivatives, connected components labeling, and union-find watersheds.
* Added graph-based image analysis, e.g. agglomerative clustering
* Included the callback mechanism described in "Impossibly Fast
C++ Delegates" by Sergey Ryazanov (needed for agglomerative clustering).
* Added many image registration functions.
* Extended the collection of multi-dimensional distance transform
algorithms by vectorial DT, boundary DT, and eccentricity transform.
* Added skeletonizeImage(), nonLocalMean(), multi-dimensional
integral images.
* Added new 2D shape features based on skeletonization and the
convex hull.
* Additional arithmetic and algebraic functions for vigra::TinyVector.
* Added vigra::CountingIterator.
* Minor improvements and bug fixes in the code and documentation.
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