VIGRA was originally designed and implemented by Ullrich Köthe. Meanwhile, many people have contributed to the effort, look at the credits page for details.
 

• VIGRA C++ reference (Version 1.9.0)
• vigranumpy reference (Python bindings)

Generische Programmierung für die Bildverarbeitung

Reusable Software in Computer Vision, in: B. Jähne, H. Haußecker, P. Geißler: "Handbook on Computer Vision and Applications", volume 3, Academic Press, 1999.

STL-Style Generic Programming with Images, in: C++ Report Magazine 12(1), January 2000

• git clone git://github.com/ukoethe/vigra.git

Official VIGRA version 1.9.0 (November 06, 2012):

• Sources (for all platforms, to be built with cmake, please read the installation instructions)
• Windows 64-bit binaries:
  • includes and libraries for Visual Studio 2010, with TIFF, JPEG, and PNG support statically linked into vigraimpex.dll, but HDF5 support disabled
  • vigranumpy Python bindings for Python 2.7 and numpy 1.6.2 (compiled with Visual Studio 2010)
• MacPort for Apple Mac OS X (still VIGRA 1.7.1, maintained by Benjamin Seppke, see his usage notes)
  
  This port finally brings the whole functionality of the new vigra 1.7.1 to the Mac. If you want to try it out, and already have macports installed, please perform a "port selfupdate" to get the new port. Here are the features of the new MacPort:

  • Supported by default:

    All image formats, which require external libs

    auto-installs libjpeg, tiff, libpng if not present

    FFTW-bindings

    auto-installs fftw-3 if not present

    HDF5 import/export

    auto-installs hdf5-18 if not present

  • Supported on demand (modeled as port variants):

    valgrind

    this also installs valgrind if not present use "port install vigra +valgrind"

    documentation creation

    this also installs doxygen if not present use "port install vigra +docs"

    vigranumpy-bindings

    The vigranumpy-bindings require boost::python, so please perform "port install boost +python27" (or +python26, resp. +python25) before installing the vigra by: "port install vigra +numpy"

• Linux binaries may be provided by your Linux distribution (and are readily created from the source by "make package")

Older versions: vigra 1.8.0, vigra 1.7.1, vigra 1.7.0, vigra 1.6.0, vigra 1.5.0, vigra 1.4.0, vigra 1.3.3, vigra 1.3.2, vigra 1.3.1, vigra 1.3.0, vigra 1.2.0, vigra 1.1.6, vigra 1.1.5, vigra 1.1.4, vigra 1.1.3, vigra 1.1.2, vigra 1.1.1, vigra 1.0
 

• STL-like OpenCV wrapper by Hirotaka Niitsuma: Interoperability between VIGRA, uBLAS and the computer vision library OpenCV
• VigraQt by Hans Meine: Interoperability between VIGRA and the Qt user interface framework

• templated image data structures for arbitrary pixel types, fixed-size vectors
• multi-dimensional arrays for arbitrary high dimensions
• pre-instantiated images with many different scalar and vector valued pixel types (byte, short, int, float, double, complex, RGB, RGBA etc.)
• 2-dimensional image iterators, multi-dimensional iterators for arbitrary high dimensions, adapters for various image and array subsets
• input/output of many image file formats: Windows BMP, GIF, JPEG, PNG, PNM, Sun Raster, TIFF (including 32bit integer, float, and double pixel types and multi-page TIFF), Khoros VIFF, HDR (high dynamic range), Andor SIF, OpenEXR
• input/output of images with transparency (alpha channel) into suitable file formats.
• comprehensive support for HDF5 (input/output of arrays in arbitrary dimensions)
• continuous reconstruction of discrete images using splines: Just create a SplineImageView of the desired order and access interpolated values and derivative at any real-valued coordinate.

• STL-style image processing algorithms with functors (e.g. arithmetic and algebraic operations, gamma correction, contrast adaptation, thresholding), arbitrary regions of interest using mask images
• image resizing using resampling, linear interpolation, spline interpolation etc.
• geometric transformations: rotation, mirroring, arbitrary affine transformations
• automated functor creation using expression templates
• color space conversions: RGB, sRGB, R'G'B', XYZ, L*a*b*, L*u*v*, Y'PbPr, Y'CbCr, Y'IQ, and Y'UV
• real and complex Fourier transforms in arbitrary dimensions, cosine and sine transform (via fftw)
• noise normalization according to Förstner
• computation of the camera magnitude transfer function (MTF) via the slanted edge technique (ISO standard 12233)

• 2-dimensional and separable convolution, Gaussian filters and their derivatives, Laplacian of Gaussian, sharpening etc.
• separable convolution and FFT-based convolution for arbitrary dimensional data
• resampling convolution (input and output image have different size)
• recursive filters (1st and 2nd order), exponential filters
• non-linear diffusion (adaptive filters), hourglass filter
• total-variation filtering and denoising (standard, higer-order, and adaptive methods)
• tensor image processing: structure tensor, boundary tensor, gradient energy tensor, linear and non-linear tensor smoothing, eigenvalue calculation etc. (2D and 3D)
• distance transform (Manhattan, Euclidean, Checker Board norms, 2D and 3D)
• morphological filters and median (2D and 3D)
• Loy/Zelinsky symmetry transform
• Gabor filters

• edge detectors: Canny, zero crossings, Shen-Castan, boundary tensor
• corner detectors: corner response function, Beaudet, Rohr and Förstner corner detectors tensor based corner and junction operators
• region growing: seeded region growing, watershed algorithm

• connected components labeling (2D and 3D)
• detection of local minima/maxima (including plateaus, 2D and 3D)
• tensor-basesd image analysis (2D and 3D)
• powerful incremental computation of region and object statistics

• point-wise transformations, projections and expansions in arbitrary high dimensions
• all functors (e.g. regions statistics) readily apply to higher dimensional data as well
• separable convolution and FFT-based convolution filters, resizing, morphology, and Euclidean distance transform for arbitrary dimensional arrays (not just 3D)
• connected components labeling, seeded region growing, watershed algorithm for volume data

• random forest classifier with various tree building strategies
• variable importance, feature selection (based on random forest)
• unsupervised decomposition: PCA (principle component analysis) and pLSA (probabilistic latent semantic analysis)

• special functions (error function, splines of arbitrary order, integer square root, chi square distribution, elliptic integrals)
• random number generation
• rational and fixed point numbers
• quaternions
• polynomials and polynomial root finding
• matrix classes, linear algebra, solution of linear systems, eigen system computation, singular value decomposition
• optimization: linear least squares, ridge regression, L1-constrained least squares (LASSO, non-negative LASSO, least angle regression), quadratic programming

• Python bindings in both directions (use Python arrays in C++, call VIGRA functions from Python)
• Matlab bindings of some functions

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de)
Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

VIGRA 1.9.0
November 06, 2012