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.
Problems found with existing digests:
Package fotoxx distfile fotoxx-14.03.1.tar.gz
ac2033f87de2c23941261f7c50160cddf872c110 [recorded]
118e98a8cc0414676b3c4d37b8df407c28a1407c [calculated]
Package ploticus-examples distfile ploticus-2.00/plnode200.tar.gz
34274a03d0c41fae5690633663e3d4114b9d7a6d [recorded]
da39a3ee5e6b4b0d3255bfef95601890afd80709 [calculated]
Problems found locating distfiles:
Package AfterShotPro: missing distfile AfterShotPro-1.1.0.30/AfterShotPro_i386.deb
Package pgraf: missing distfile pgraf-20010131.tar.gz
Package qvplay: missing distfile qvplay-0.95.tar.gz
Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden). All existing
SHA1 digests retained for now as an audit trail.
* VIGRA got a tutorial.
* Significant simplification of the API: MultiArrayView arguments can now be passed to functions directly. The old syntax with Argument Object Factories (srcImageRange(), srcMultiArray() and relatives) remains valid, but is only required when the arguments are old-style BasicImages.
* Made StridedArrayTag the default for vigra::MultiArrayView .
* Added an efficient multi-dimensional vigra::GridGraph class which support both the LEMON and boost::graph APIs.
* Generalized various algorithms to arbitrary dimensions (gaussianGradientMultiArray(), hessianOfGaussianMultiArray(), gaussianDivergenceMultiArray(), localMinima(), localMaxima(), labelMultiArray(), watershedsMultiArray()).
* Added slicSuperpixels() for arbitrary dimensions.
* Added automatic differentiation (see vigra::autodiff::DualVector).
* Added nonlinearLeastSquares() using the Levenberg-Marquardt algorithm and automatic differentiation. More information about the changes can be found on the changelog page.
* Flexible incremental computation of image and region statistics via Feature Accumulators and corresponding Python bindings (see vigra.analysis.extractFeatures() and vigra.analysis.extractRegionFeatures()).
* Simultaneous iteration over several arrays via vigra::CoupledScanOrderIterator.
* Import and export of images with transparency: importImageAlpha() and exportImageAlpha().
* Image denoising by total variation filtering, see Non-linear Diffusion and Total Variation and example total_variation.cxx.
* Fixed Python bindings of Unsupervised Decomposition.
* Extended vigra::SplineImageView to handle vigra::TinyVector pixels.
* Various convenience functions for vigranumpy's VigraArray.
* Minor improvements and bug fixes in the code and documentation.
* Various extensions to Local Minima and Maxima (3D algorithms, on-the-fly
thresholding).
* Added vigra::BucketQueue, vigra::MappedBucketQueue.
* Refactored and extended 2D watershed algorithms (especially
watershedsRegionGrowing()).
* Added the vigra::Quaternion class.
* Added Unsupervised Decomposition.
* Added mathematical functions (even(), odd(), gamma(), loggamma(), legendre(),
besselJ(), besselY(), linearSequence(), indexSort(), inversePermutation(),
applyPermutation(), checksum())
* Implemented wrapper of the FFTW library to support arbitrary dimensional
Fourier transforms (see fourierTransform() and vigra::FFTWPlan) and FFT-based
convolution (see convolveFFT() and vigra::FFTWConvolvePlan) and refactored
vigra::FFTWComplex.
* Added cannyEdgelListThreshold(), cannyEdgelList3x3Threshold().
* Added capability to handle subarrays and anisotropic resolution to
separableConvolveMultiArray() and related Gaussian filters. Added windowRatio
parameters to vigra::Kernel1D::initGaussian() and
vigra::Kernel1D::initGaussianDerivative()
* Added vigra::StridedScanOrderIterator and corresponding
vigra::MultiArrayView::begin().
* Extended vigra::MultiArrayView. Added vigra::Shape1 ... vigra::Shape5
convenience typedefs.
* Implemented vigra::multi_math (arithmetic and algebraic functions for
multi-dimensional arrays).
* Extended the vigra::HDF5File class.
* Improved and documented the Timing macros for runtime measurements.
* Added support for the OpenEXR image format and multi-page TIFF. Improved
support for the SIF format.
* vigranumpy: added axistags and completly re-implemented VigraArray and the
conversion between Python and C++ arrays in terms of axistags.
* Minor improvements and bug fixes in the code and documentation.
* 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.
