No abstract

The concept of an algorithm developed for the segmentation of a road border from the content of an image produced by a forward looking TV camera mounted on a moving vehicle is presented in this paper. The extraction of a road boundary is an important step in the context of autonomous vehicle guidance, enabling further calculations of distance from the border, direction of a road, etc. The main idea behind this approach is that the texture of a road is different enough in comparison to the textures characterizing the surrounding environment, allowing the separation of the overall image into a few distinguishable regions. The segmentation algorithm combines the texture descriptors of a statistical nature and the ones based on a grey level co-occurrence matrix. The significance of this work is mainly in the practical verification of the proposed algorithm and in the testing of the real limits of its application.

No abstract

In military application target tracking has always been an interesting and challenging problem. Nowadays, it has also found its place in civil applications, especially concerning surveillance and monitoring. Until recently, thermal imagery (image is formed based on infrared spectrum radiation) was considered only in military applications because of the price and size of cameras. Also, thermal image quality was not as good as TV (image is formed based on visual spectrum radiation) camera image. The situation has changed, and thermal cameras are now widely used in many kinds of applications. Thermal image is different from TV camera image, as it measures temperature difference between objects and background. Therefore, it has an advantage over television cameras, since it can be used in low light conditions and in dark. This paper examines the options for a coarse and quick algorithm for rough target locating in thermal image, where the target is a small Unmanned Aerial Vehicle (UAV). Three different feature descriptor algorithms are tested in thermal imagery target tracking. Feature descriptor methods are widely used in visual imagery, but the goal of this paper is to examine their usage in thermal imagery. That is why three different feature descriptor algorithms from three different families are tested: FREAK (Fast Retina Keypoint), SURF (Speeded Up Robust Features) and MSER (Maximally Stable Extremal Regions). The algorithms are tested in case of translation, rotation, blur and size change of an object of interest, as well as in the case of noisy image. Since none of the tested methods works great in different situations, new, multi-stage algorithm is proposed. This algorithm is based on MSER and SURF algorithm combination, with a goal to use the advantages of each of them in different real situations. The obtained results show that the new, multi-stage algorithm has got the best performance among the group of the tested methods. All the algorithms are implemented in Matlab software.

No abstract