Center for Informatics and Computing
Center for Informatics and Computing
Trinh D.-H.,Center for Informatics and Computing |
Luong M.,University of Paris 13 |
Dibos F.,University of Paris 13 |
Rocchisani J.-M.,University of Paris 13 |
And 3 more authors.
2014 IEEE International Conference on Image Processing, ICIP 2014 | Year: 2014
Denoising is an essential application to improve image quality, especially in medical imaging. This paper introduces an example and patch-based learning method for reducing Gaussian noise and Poisson noise which often appear in medical imaging modalities using ionizing radiation. In the proposed method, denoising is performed by learning the regression model based on a set of the nearest neighbors of a given noisy patch, with the help of a given set of standard images. The method is evaluated and compared to several state-of-the-art denoising methods. The obtained results confirm its efficiency, especially for heavy noise. © 2014 IEEE.
Eded A.,Josip Juraj Strossmayer University of Osijek |
Loncaric Z.,Josip Juraj Strossmayer University of Osijek |
Horvat D.,Josip Juraj Strossmayer University of Osijek |
Skala K.,Center for Informatics and Computing
Poljoprivreda | Year: 2010
Visualization of multivariate multidimensional data sets is a challenging task, especially without use of adequate tools and methods. In the last few years, parallel coordinate plots became quite popular and accepted as a very efficient multivariate visualizationtechnique. The aim of this paper was to explore how parallel coordinates can be used in analysis of winter wheat quantitative traits. Data set is obtained from experiment set up by a completely randomized design with two treatments and four replicates. Ten variables (plant height, spike length, stem length, plant weight, spike weight, grain weight per spike, 1000 kernel weight, number of fertile and sterile spikelets per spike and total number of spikelets per spike) and fifty-five winter wheat genotypes were analysed in this paper. In parallel coordinate plots observations are shown as series of unbroken lines, passing through parallel axes, where each axes represents a different variable. Advantage ofparallel coordinates, compared to other visualization techniques, is that they can represent multivariate data in two dimensions. From such representation, outliers and grouping among observations are easily detectable. Correlation among variables can also be easily detected from such representation. Although parallel coordinates cannot efficiently explore details, they are a good technique for visualization of multivariate data sets and they can be used for exploratory analysis of wheat quantitative trait.
Bojovic V.,Center for Informatics and Computing |
Lucic B.,Ruder Boskovic Institute |
Skala K.,Center for Informatics and Computing |
Grubisic I.,Center for Informatics and Computing
MIPRO 2010 - 33rd International Convention on Information and Communication Technology, Electronics and Microelectronics, Proceedings | Year: 2010
This service is made in order to achieve more efficient insight into the nature of hydrophobic or hydrophilic forces in proteins. It enables to visualize details and specificity in contacts between amino acid residues in proteins. In addition, it is possible to visualize contributions of inter-residue contacts depending on selected physical and chemical properties of amino acid residues (current version is based on the hydrophobicity scale intoroduced by J. Kyte and R. F. Doolittle, J. Mol. Biol., vol. 157, p. 105-132, 1982). Choosing one amino acid residue, distances between its side chain and side chains of all amino acid residues within the sphere of chosen radius can be visualized. Current version of database included in the server contains only 100 proteins crucial for performing research on modeling of protein folding rates and some antimicrobial polypetides, but it will be enlarged by new sequences. Visualization is available in png, povray, and vrml format, and the PDB format output is also available. Application can be found at http://mamlaz.irb.hr/ pdbggg.