The proposed algorithm matches local coordinates of each image with coordinates of the map by extracting roads’ line segments from the image and finding geometric transformation successfully matching the roads’ segments and the map’s ones. Parameters estimation is based on RANSAC algorithm which analyses the segments’ location and orientation.
In this paper the authors compared the accuracy of several stereo matching algorithms using problem-oriented metrics developed by the authors earlier for obstacle detection. For comparison we have chosen the most computationally effective open-source algorithms, suitable for using in autonomous systems with limited processor capacities. The quality of the algorithms was compared on the public dataset KITTI Stereo Evaluation 2015. The hypothesis that the problemoriented metric of the stereo matching quality will lead to a different ranking than the universal metric, was not confirmed. At the same time, our measurements of the algorithms execution time showed results significantly different from those stated on KITTI portal.
In this paper we study the problem of combining UAV obtained optical data and a coastal vector map in absence of satellite navigation data. The method is based on presenting the territory as a set of segments produced by color-texture image segmentation. We then find such geometric transform which gives the best match between these segments and land and water areas of the georeferenced vector map. We calculate transform consisting of an arbitrary shift relatively to the vector map and bound rotation and scaling. These parameters are estimated using the RANSAC algorithm which matches the segments contours and the contours of land and water areas of the vector map. To implement this matching we suggest computing shape descriptors robust to rotation and scaling. We performed numerical experiments demonstrating the practical applicability of the proposed method.
Digital X-ray imaging became widely used in science, medicine, non-destructive testing. This allows using modern digital images analysis for automatic information extraction and interpretation. We give short review of scientific applications of machine vision in scientific X-ray imaging and microtomography, including image processing, feature detection and extraction, images compression to increase camera throughput, microtomography reconstruction, visualization and setup adjustment.
Obtaining high quality images from Computed Tomography (CT) is important for correct image interpretation. In this paper, we propose novel procedures that can be used for a quantitative description of the degree of artifact expressiveness in CT images, and show that the use of this type of metric allows to assess the dynamics of image degradation. We perform different image reconstruction tests in order to analyse our approach, and the obtained results confirm the usefulness of the proposed method. We conclude that the use of the proposed estimates allows moving from image quality assessment based on visual scoring to a quantitative approach and consequently to support a CT setup providing high quality reconstructed images obtained by appropriate changes of the reconstruction parameters or algorithms.
In this paper real time classification method for two-channel X-ray radiographic diamond separation is discussed. Proposed method does not require direct hardware calibration but uses sample images as a train dataset. It includes online dynamic time warping algorithm for inter-channel synchronization. Additionally, algorithms of online source signal control are discussed, including X-ray intensity control, optical noise detection and sensor occlusion detection.