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...It also requires Statistics Toolbox (for some simple random number generator) and Image Processing Toolbox (for reading image data). The code is extremely compact. Minimizing code length is a major goal. As a result, the core of the algorithms can be easily spotted. Many tricks for speeding up Matlab code are applied (e.g. vectorization, matrix factorization, etc.). Usually, functions in this package are orders faster than Matlab builtin ones (e.g. kmeans). Many tricks for numerical stability are applied, such as computing probability in logrithm domain, square root matrix update to enforce matrix symmetry.

AlgART Java libraries for processing arrays and matrices are open-source product, distributed under MIT license. So, anyone can use them for free without any restrictions. Main features: 63-bit addressing of array elements (64-bit long int indexes), memory model concept (allowing storing data in different schemes from RAM to mapped disk files), wide usage of lazy evaluations, built-in multithreading optimization for multi-core processors, wide set of image processing algorithms over...

Image Processing software for combining information from images of the same scene, but with illumination variations or small changes. This work is the result of the coursework of an Image Processing regular course at IMPA.

Testlab for Image Processing: You can enter formulas like "r = r + b/2; g=g*2; b=b/2" and watch/store the Result. Alternatively cou can run a convolution matrix on the source image. Implemented in Java, fully GUIed, avg. performance.