Academic literature on the topic 'Алгоритм SIFT'

В роботі запропоновано інформаційну технологію дешифрування антропогенних змін на різночасових супутникових зображеннях високої просторової здатності. Автоматизована технологія включає методи попередньої обробки знімків (підвищення інформативності, корекції), алгоритм уточнення геопривʼязки перед пошуком змін на різночасових зображеннях високого просторового розрізнення, який ґрунтується на основі методу SIFT. Результати досліджень свідчать про те, що застосування інформаційної технології дозволяє виявляти антропогенні зміни на різночасових зображеннях.

The article considers the accuracy of floor models creation in large span structures with the use of unmanned aerial vehicles for the purpose of determining their deformations. There were performed 4 aero shootings of Water Sports Palace in Dushambe, which were processed by SIFT algorithm. Syn-chronically with the UAV shooting there was performed the measurements on deformation marks within the frames of geodetic monitoring. There was performed the statistical analysis of models accu-racy by method of interpolation of spherical function, which showed that 80% of points has devia-tions less than 30 mm, and the rest 20% has deviations up to 80 mm. The use of collocation method for smoothing optimal filtering allowed to decrease the differences between settlement surfaces ob-tained by geodetic measurement and UAV shooting up to 26mm., which is 10 % of acceptable devia-tions of this object. It was found out that there is a necessity of further investigations of SIFT algo-rithm and filtering its results for the purpose of increasing the accuracy of object’s models.

Despite the increasing popularity of process automation, modern video surveillance systems still require constant human involvement to establish the fact of dangerous situations. But at present, systems are becoming more complex, this leads to an increase in threats and it is no longer possible for the operator to keep track of all emerging threats. In addition, in the field of video surveillance, tasks have been added that a person can no longer control just by watching video cameras. In this connection, you need to automate the process. Methods that provide maximum detection stability for small object movements, zoom changes, turning the object at a small angle, and changing lighting are based on describing the image at specific points. A special point is a point that has a number of key features that distinguish it from many other points in the image. Special points are the main characteristics of the object in the video surveillance system. The best object recognition algorithms based on this principle are the SURF and SIFT algorithms. These algorithms search for the direct occurrence of the reference image in relation to the observed one. The article discusses algorithms for detecting objects in an image based on the description of the image by special points. A comparison of SIFT and SURF algorithms, the analysis highlighted particular points in the recognition of each object, error analysis AI Node in identifying objects in the video stream.

While solving the tracking of objects for infrared range images the obtaining of the necessary volume relevant basis for neuron network training is problematic enough. The main obstacles are the absence of the far infrared range images base and the complexity of calibration of cameras of various manufacturers for composing the base. Due to these causes and the complexity of marking the borders of the given range objects using the neuron networks, at the moment, is not expedient. In the paper the principles of object tracking on the infrared range images, based on obtaining the key points using the SIFT, SURF, ORB algorithms, have been considered. The specific features of the given methods have been distinguished, and also, the specifics of far infrared range images and of the objects observed on it, has been emphasized. The analysis of the approaches to preserve the features of tracking an object in transition to a new frame of a video image has been performed, their main advantages and disadvantages have been revealed. Based on the obtained results the modernization of the tracking algorithm using the Jarvis algorithm modification has been proposed. The estimation of the influence on speed of additional processing in the tracking algorithm has been experimentally performed. The proposed approach to tracking an object on the infrared image, composed on the ORB algorithm key points and the application of the modified Jervis algorithm, is invariant to turns and scaling of the sought for object and allows to watch it in conditions of high nonhomogeneity of the background and presence of similar objects. The application of the developed algorithm permits to avoid a considerable loss of speed with a significant increase of the object tracking efficiency.

Image panorama is a process of combining two or more images to form one single image. Image stitching is a process of creating an image panorama from a set of images with overlapped fields. Image stitching faces many challenges such as images corrupted by noise, indexing a large number of images, high image resolution, and presence of parallax and scene motion.