The research reported in the paper aims the development of some methodologies for noise removal in image restoration. In real life, there is always some kind of noise present in the observed data. Therefore, it has been proposed that the ICA model used in image restoration should include noise...

In this paper, region based stereo matching algorithms are developed for extraction depth information from two colour stereo image pair of waste bin. Depth calculation technique from a disparity map is a key part of this study. A filter eliminating unreliable disparity estimation was used for...

The detection of remote homologies is of major importance for molecular biology applications like drug discovery. The problem is still very challenging even for state-of-the-art probabilistic models of protein families, namely Profile HMMs. In order to improve remote homology detection we...

In recent years, new remote-sensed technologies, such as airborne and terrestrial laser scanner, have improved the detail and the quality of topographic information, providing topographical high-resolution and high-quality data over larger areas better than other technologies. A new generation...

This study aims at evaluating the accuracy of mitosis detection on multispectral histopathological images by developing a solution specifically designed to take advantage of multi-spectral information. The proposed framework includes a selection of spectral bands and focal plane, detection of...

Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new...

A lot of alternatives and constraints have been proposed in order to improve the Fisher criterion. But most of them are not linked to the error rate, the primary interest in many applications of classification. By introducing an upper bound for the error rate a criterion is developed which can...

An important feature of neural networks is the ability they have to learn from their environment, and, through learning to improve performance in some sense. In the following we restrict the development to the problem of feature extracting unsupervised neural networks derived on the base of the...

Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach to direct estimation of the projector on the target...