Dynamical Pattern Vector in Pattern Recognition with the Use of Thermal Images

The goal of the following paper was to develop the methodology of object tracking in adverse conditions. Suddenly appearing clouds, fog or smoke could be the examples of atmospheric conditions. We used thermal and visible images in each moment during object tracking. We computed the pattern vectors of the tracked object on the basis of the visual and thermal images separately. The pattern vector and current feature vector for an image of a given type are used to compute the distance between the object pattern vector and feature vector calculated for a given location of the aperture. It is calculated for both: the visual and thermal image. The crux of the proposed method was the algorithm of selection which distance (for visual or thermal image) was used for object tracking. It was obtained by multiplying the values of the distances between a pattern vector and current feature vector by some coefficients (different for thermal and visual images). The values of these coefficients depended on the usefulness of a given type of an image for pattern recognition. This usefulness was defined by the variability of the particular pixels in the image which is represented by calculating gradient in the image. On top of that, this study presented the examples of the object recognition by means of the developed method

The Fusion of the Visual and Thermal Images on the Basis of Determining the Image Fragments which Contain Essential Details

The aim of the following study was to develop a procedure which guarantees the data fusion of thermal and visual images. The first stage of the proposed algorithm consisted of images acquisition which guaranteed that the same parts of images represented the same parts of the observed terrain. The second stage depended on previous information about the searched object features. Two different situations were considered herein. In the case when we had the searched object’s feature vector for both representations of a searched object, we could conduct the pattern recognition for each image. It was conducted separately for visual and thermal images. In this way, we obtained the important parts of the images which should be represented in a fused image. The other case examined in the paper, considered the situation in which we did not have the formalised information about the object. In this case, it was necessary to analyse whole images in order to define the potential parts of the images where the object could be found. This analysis should be helpful for an operator to indicate the parts of the images where there are some artefacts which can be the elements of the searched object. Therefore, in this case, the second stage of the algorithm consisted in calculating the local features of the images. These features constituted grey scale gradient computed for the pixels inside the aperture. This study presented the examples of the fused images obtained by means of the developed method

Vision based systems for UAV applications

This monograph is motivated by a significant number of vision based algorithms for Unmanned Aerial Vehicles (UAV) that were developed during research and development projects. Vision information is utilized in various applications like visual surveillance, aim systems, recognition systems, collision-avoidance systems and navigation. This book presents practical applications, examples and recent challenges in these mentioned application fields. The aim of the book is to create a valuable source of information for researchers and constructors of solutions utilizing vision from UAV. Scientists, researchers and graduate students involved in computer vision, image processing, data fusion, control algorithms, mechanics, data mining, navigation and IC can find many valuable, useful and practical suggestions and solutions. The latest challenges for vision based systems are also presented