"Railway semantic segmentation is the task of highlighting rail blades in images taken from ego-view train. Solving this allows for further image processing on rails, which can be used more complex problems such as switch or fault detection. In paper we approach railway using two deep architectures U-Net family, and ResUNet++, most comprehensive dataset available at time writing scene, namely R...

This paper presents an unsupervised dynamic colour texture segmentation method with unknown and variable number of texture classes. Single regions with dynamic textures can furthermore change their location as well as their shape. Individual dynamic multispectral texture mosaic frames are locally represented by Markovian features derived from four directional multispectral Markovian models recu...

Automatic and accurate polyp segmentation plays an essential role in early colorectal cancer diagnosis. However, it has always been a challenging task due to 1) the diverse shape, size, brightness other appearance characteristics of polyps, 2) tiny contrast between concealed polyps their surrounding regions. To address these problems, we propose lesion-aware dynamic network (LDNet) for segmenta...

In this paper we present an approach to detect traffic guidance signs and recognise the structure of junction information on them. The detection algorithm is based on using differently exposed images. These images are combined into one using tone mapping technique in order to minimize effects of bad environment conditions and low dynamic range of CCDcameras. This technique allows robust sign de...

the ultimate goal of power system operation and planning is to increase power system reliability which enforces interconnected operation of power system. as a result of power system interconnection, the inter-area oscillation under different disturbances may cause power system partial or total blackout. dc-segmentation of interconnected power systems is a solution in which the topology of the n...

We address the problem of segmenting a sequence of images of natural scenes into disjoint regions that are characterized by constant spatio-temporal statistics. We model the spatio-temporal dynamics in each region by Gauss-Markov models, and infer the model parameters as well as the boundary of the regions in a variational optimization framework. Numerical results demonstrate that – in contrast...