  Computer vision has become a major source of information for autonomous
navigation of robots of various types, self-driving cars, military robots and
mars/lunar rovers are some examples. Nevertheless, the majority of methods
focus on analysing images captured in visible spectrum. In this manuscript we
elaborate on the problem of segmenting cross-country scenes captured in IR
spectrum. For this purpose we proposed employing salient features. Salient
features are robust to variations in scale, brightness and view angle. We
suggest the Speeded-Up Robust Features as a basis for our salient features for
a number of reasons discussed in the paper. We also provide a comparison of two
SURF implementations. The SURF features are extracted from images of different
terrain types. For every feature we estimate a terrain class membership
function. The membership values are obtained by means of either the multi-layer
perceptron or nearest neighbours. The features' class membership values and
their spatial positions are then applied to estimate class membership values
for all pixels in the image. To decrease the effect of segmentation blinking
that is caused by rapid switching between different terrain types and to speed
up segmentation, we are tracking camera position and predict features'
positions. The comparison of the multi-layer perception and the nearest
neighbour classifiers is presented in the paper. The error rate of the terrain
segmentation using the nearest neighbours obtained on the testing set is
16.6+-9.17%.
