Volume 24 (2016)


Recent Submissions

Svoboda, Pavel , Hradiš, Michal , Bařina, David , Zemčík, Pavel
Compression artifacts removal using convolutional neural networks

This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other&...

Maisch, Sebastian , Skånberg, Robin , Ropinski, Timo
A comparative study of mipmapping techniques for interactive volume visualization

Many large scale volume visualization techniques are based on partitioning the data into bricks, which are stored and rendered using mipmaps. To generate such mipmaps, in most cases an averaging is applied such that an area in a lower mipmap level is presented by the area...

Da-Young, Lee , Kyung-Rim, Kim , Taeyong, Kim , Hwan-Gue, Cho
Comparison-specialized visualization model for whole genome sequences

Analyzing and visualizing the whole genome sequence is very important to finding genetic evolution. Many researchers have used 2D or 3D DNA random walk plots to study short DNA sequences. However, visualizing a whole genome sequence is difficult because of overlapping, self-intersection,...

Azam, Samiul , Gavrilova, Marina
Gender prediction using individual perceptual image aesthetics

Images have rarely been used for psychological behavior analysis or for person identification in the information technology domain of research. In this paper, we present one of the first methods that allows to accurately predict gender from a collection of person’s favorite images. ...

Ji-Won, Lee , Byung-Uk, Lee
Fisheye lens correction by estimating 3D location

Since a fisheye lens can capture a wide angle scenery, it is broadly used for surveillance or outdoor sports. However, acquired images suffer from severe geometric distortions. Most of the existing distortion correction algorithms depend on linear features: images of linear features are...