Robust estimation of subspace coefficients. This package contains Matlab functions, which perform robust estimation of subspace coefficients in PCA, CCA and LDA methods.
NEW! Version 3.4 This is a Matlab research code that is based on the papers on Online Kernel Density Estimation with Gaussian Kernels and Online Discriminative Kernel Density Estimation with Gaussin Kernles. The code essentially demonstrates estimation of a Gaussian Mixture Model from a stream of data.
The code is a minimal implementation of a batch kernel density estimator. Since the code is based on our new bandwidth estimator, it allows KDE construction even from preclustered/compressed sets of samples and weighted data. This is also a minimal demonstration of the general bandwidth estimator proposed in “Online Kernel Density Estimation with Gaussian Kernels”.
This is a research code for 2D KDE that is based on the paper “Online Kernel Density Estimation with Gaussian Kernels”.
This is a demo code for the unscented Hellinger distance between a pair of Gaussian mixture models. The code follows the derivation of the multivariate unscented Hellinger distance introduced in [1]. Unlike the Kullback-Leibler divergence, the Hellinger distance is a proper metric between the distributions and is constrained to interval (0,1) with 0 meaning complete similarity and 1 complete dissimilarity.
This is a Matlab implementation of the Bayes spectral based measure of camera focus using a discrete cosine transform.


Several laboratory software projects have been moved to GitHub to promote the contributions from other sources.


  • LUIS34 - Ljubljana Urban Image Data Set
  • DUIS131 - Darmstadt Urban Image Data Set
  • GUIS107 - Graz Urban Image Data Set
  • FIDS30 - Fruit Image Data Set