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576fed080a
New version 1.11.0.
Changes:
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.
9 lines
405 B
Plaintext
9 lines
405 B
Plaintext
VIGRA stands for "Vision with Generic Algorithms". It's a novel computer
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vision library that puts its main emphasize on customizable algorithms
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and data structures. By using template techniques similar to those in
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the C++ Standard Template Library, you can easily adapt any VIGRA
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component to the needs of your application, without thereby giving up
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execution speed.
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WWW: http://ukoethe.github.io/vigra/
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