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
• 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

• hg clone http://www.informatik.uni-hamburg.de/~meine/hg/vigra

Official VIGRA version 1.7.0 (April 20, 2010):

• Sources (for all platforms, to be built with cmake)
• Windows binaries:
  • includes and libraries for Visual Studio 2008 Express (Visual C++ 9.0), with TIFF, JPEG, and PNG support statically linked into vigraimpex.dll, but HDF5 support disabled
  • vigranumpy Python bindings for Python 2.6 (compiled with Visual Studio 2008 Express)
• Linux binaries may be provided by your Linux distribution (and are readily created from the source by "make package")

Older versions: 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), Khoros VIFF, HDR (high dynamic range)
• 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 transform, 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 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
• tensor image processing: structure tensor, boundary tensor, gradient energy tensor, linear and non-linear tensor smoothing, eigenvalue calculation etc.
• 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)
• tensor-basesd image analysis
• region 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 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

• special functions (error function, splines of arbitrary order, integer square root, chi square distribution, elliptic integrals)
• random number generation
• rational and fixed point numbers
• 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 1.7.0 (April 20, 2010)