Pavel Vácha

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This paper describes method for analysis of the texture created by retinal nerve fibers (RNF) via Markov Random Fields. The Causal Autoregressive Random (CAR) model is used to create a feature vector describing the changes in texture due to losses in RNF layer. It is shown that features based on CAR model can be used for discrimination between healthy and(More)
We introduce a new standalone widely applicable software library for feature selection (also known as attribute or variable selection), capable of reducing problem dimensionality to maximize the accuracy of data models, performance of automatic decision rules as well as to reduce data acquisition cost. The library can be exploited by users in research as(More)
This paper surveys the current best texture representations and studies their application for a texture similarity measure development. A simple experiment that evaluates texture similarity is proposed and the performance of several most advanced texture features is verified on it. In order to eliminate the influence of spectral information monospectral(More)
Content-based image retrieval (CBIR) systems, target database images using feature similarities with respect to the query. We introduce fast and robust image retrieval measures that utilise novel illumination invariant features extracted from three different Markov random field (MRF) based texture representations. These measures allow retrieving images with(More)
A visual appearance of natural materials fundamentally depends on illumination conditions, which significantly complicates a real scene analysis. We propose textural features based on fast Markovian statistics, which are simultaneously invariant to illumination colour and robust to illumination direction. No knowledge of illumination conditions is required(More)