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

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 1.6.0 (Aug 13, 2008):

• Sources (for all platforms)
• Windows binaries (with sources) for Visual C++ 8.0 (Visual Studio 2005)
• MacPort for Apple Mac OS X
• Linux binaries may be provided by your Linux distribution

Older versions: 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)
• morphological filters and median (disk structuring elements)
• 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
• 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

• 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, linear least squares, ridge regression

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

VIGRA 1.6.0 (Aug 13, 2008)