Allocation of edges by a structured detector on an image obtained from a shipborne optical-electronic surveillance system

Встановлено, що результат обробки зображень, що отримані з бортових систем оптикоелектронного спостереження, залежить від якості методу сегментування зображення, що, в свою чергу, поставляє перед розробниками систем обробки зображень проблему розробки методик, методів та вибору показників оцінки якості сегментування зображень. Розглянута можливість використання структурованого детектору з методом машинного навчання Random Forest для виділення границь на зображенні, що отримане з бортової системи оптико-електронного спостереження. Проведено експериментальне дослідження щодо виділення границь структурованим детектором на зображенні, що отримане з бортової системи оптико-електронного спостереження, з використанням машинного навчання Random Forest. Відмічаються основні переваги використання методу машинного навчання Random Forest при виділенні границь об’єктів на оптико-електронному зображенні.Установлено, что результат обработки изображений, которые получены с бортовых систем оптикоэлектронного наблюдения, зависит от качества метода сегментации изображения, что, в свою очередь, ставит перед разработчиками систем обработки изображений проблему разработки методик и выбора показателей оценки качества сегментации изображений. Рассмотрена возможность использования структурированного детектора с методом машинного обучения Random Forest для выделений границ на изображении, полученном с бортовой системы оптикоэлектронного наблюдения. Проведено экспериментальное исследование выделения границ структурированным детектром на изображении, полученном с бортовой системы оптико-электронного наблюдения, с использованием машинного обучения Random Forest. Отмечаются основные преимущества использования метода машинного обучения Random Forest при выделении границ на оптико-электронном изображении.It has been established that the result of processing images obtained from on-board optical-electronic surveillance systems depends on the quality of the image segmentation method, which in turn poses the problem of the development of techniques and the selection of indicators for assessing the quality of image segmentation before the developers of imaging systems. The possibility of using a structured detector with the method of machine learning Random Forest for isolating boundaries on an image obtained from an on-board optical-electronic surveillance system is considered. An experimental study of the delineation of boundaries by a structured detector in an image obtained from an on-board optical-electronic surveillance system using Random Forest's computer training was carried out. The main advantages of using the method of machine learning Random Forest when allocating borders on the opto-electronic image are noted

Segmentation of Optical-electronic Images From On-board Systems of Remote Sensing of the Earth by the Artificial Bee Colony Method

It was established that it is not possible to apply the known methods of image segmentation directly to segmentation of optical-electronic images of on-board systems of remote sensing of the Earth. We have stated the mathematical problem on segmentation of such images. It was established that the result of segmentation of images of on-board systems of remote sensing of the Earth is separation of an image into artificial objects (objects of interest) and natural objects (a background). It has been proposed to use the artificial bee colony method for segmentation of images. We described the essence of the method, which provides for determination of agents positions, their migration, conditions for stopping of an iteration process by the criterion of a minimum of a fitness function and determination of the optimal value of a threshold level. The fitness function was introduced, which has the physical meaning of a sum of variance brightness of segments of a segmented image. We formulated the optimization problem of image segmentation of an on-board optical-electronic observation system. It consists in minimization of a fitness function under certain assumptions and constraints.The paper presents results from an experimental study on application of the artificial bee colony method to segmentation of an optical-electronic image. Experimental studies on segmentation of an optical-electronic image confirmed the efficiency of the artificial bee colony method. We identified possible objects of interest on the segmented image, such as tanks with oil or fuel for aircraft, airplanes, airfield facilities, etc.The visual assessment of the quality of segmentation was performed. We calculated errors of the first type and the second type. It was established that application of the artificial bee colony method would improve the quality of processing of optical-electronic images. We observed a decrease of segmentation errors of the first type and the second type by the magnitude from 7 % to 33 % on averag

Segmentation of the Images Obtained From Onboard Optoelectronic Surveillance Systems by the Evolutionary Method

The essence of the simpler evolutionary method of image segmentation which relates to ant methods was set forth. The image segmentation process was presented as a set of areas in which agents (ants) move. Probability of transition from one turning point of the route to another was determined taking into account attractiveness of the route and concentration of pheromones on it. A timely convergence of decisions (choice of the same route by the agents) is processed by the use of feedback, i.e. evaporation of pheromones. The parameters setting pheromone weight and attractiveness of the area were calculated. The routes which are the most attractive according to the selected criteria (with the maximum concentration of pheromone) were determined. Unattractive routes disappear with a gradual "drying" of pheromone on such routes. When checking function ability of the simpler evolutionary segmentation method, it was found that implementations of this method with obviously unsuccessful results are possible.Essence of the advanced evolutionary method of image segmentation as improvement of the simpler evolutionary method was outlined. In the improved method, only the best agents increase the level of pheromone on their routes. The level of pheromone on the routes is limited. An expression has been obtained for renewal of pheromone levels. The best route may be either the iteration best or the best-so-far (found since the start of the method) route.In contrast to the simpler evolutionary method, an optimal route of agent movement was found during segmentation of images in all implementations with the use of the advanced evolutionary method.Experimental studies of segmentation of the images obtained from the onboard systems of optoelectronic surveillance using the evolutionary method have been carried out. As an example, possible objects of interest were defined in the segmented image and it was established that the outlined contours of the main objects of interest coincide with the boundaries of the objects in the original image. Presence of a large number of outlined contours of small-sized objects in the segmented image was pointed out and an example of such area was given. Visual estimation of efficiency of application of the evolutionary method was mad

Selection of color space for segmentation of images received from on-board optic-electronic observation systems

Проаналізовані атмосферні фактори, що впливають на формування зображення в космічній системі оптико-електронного спостереження. Встановлюється, що сегментування кольорового зображення, що отримується оптико-електронною системою спостереження, потребує вибору моделі кольорового простору. Встановлено, що сегментування зображення залежить від яскравості зображення та в меншому ступеню від кольоровості та насиченості. У зв’язку з цим, основна увага повинна бути приділена каналу яскравості зображення. Тому, для сегментування кольорових зображень найбільш підходять кольорові простори з явно вираженим каналом яскравості. Сформульовано методика сегментування кольорових зображень, що отримані з бортових систем оптико-електронного спостереження.Проанализированы атмосферные факторы, которые влияют на формирование изображения в космической системе оптико-электронного наблюдения. Установлено, что сегментация цветного изображения, полученного космической системой оптико-электронного наблюдения, требует выбора модели цветового пространства. Установлено, что сегментация изображения зависит от яркости изображения и, в меньшей степени, от цветности и насыщенности. В связи с этим, основное внимание необходимо уделить каналу яркости изображения. Поэтому, для сегментации цветных изображений наиболее подходят цветовые пространства с явно выраженным каналом яркости. Сформулирована методика сегментации цветных изображений, полученных с бортовых систем оптико-электронного наблюдения.The atmospheric factors that influence the formation of images in the cosmic system of optoelectronic observation are analyzed. It is established that the segmentation of the color image obtained by the cosmic optic-electronic surveillance system requires the choice of a color space model. It is established that the segmentation of the image depends on the brightness of the image and, to a lesser extent, on color saturation and saturation. In this regard, the main attention should be paid to the image brightness channel. Therefore, for color image segmentation, color spaces with a clearly defined luminance channel are most suitable. The technique of segmentation of color images obtained from on-board optic-electronic surveillance systems is formulated

Devising A Method for Processing the Image of A Vehicle's License Plate When Shooting with A Smartphone Camera

This paper reports an improved method for processing the image of a vehicle's license plate when shooting with a smartphone camera. The method for processing the image of a vehicle's license plate includes the following stages: – enter the source data; – split the video streaming into frames; – preliminary process the image of a vehicle's license plate; – find the area of a vehicle's license plate; – refine character recognition using the signature of a vehicle's license plate; – refine character recognition using the combined results from frames in the streaming video; – obtain the result of processing. Experimental studies were conducted on the processing of images of a vehicle's license plate. During the experimental studies, the license plate of a military vehicle (Ukraine) was considered. The original image was the color image of a vehicle. The results of experimental studies are given. A comparison of the quality of character recognition in a license plate has been carried out. It was established that the improved method that uses the combined results from streaming video frames works out efficiently at the end of the sequence. The improved method that employs the combined results from streaming video frames operates with numerical probability vectors. The assessment of errors of the first and second kind in processing the image of a license plate was carried out. The total accuracy of finding the area of a license plate by known method is 61 % while the improved method's result is 76 %. It has been established that the minimization of errors of the first kind is more important than reducing errors of the second kind. If a license plate is incorrectly identified, these results would certainly be discarded at the character recognition stage

Construction of Methods for Determining the Contours of Objects on Tonal Aerospace Images Based on the Ant Algorithms

A method has been proposed for determining contours of objects on tonal aerospace images based on ant algorithms. The method, in contrast to those already known, takes into consideration patterns in the image formation; the ant algorithm is used for determining the contours. Determining an object's contours in the image has been reduced to calculating the fitness function, the totality of agents' motion areas, and the pheromone concentration along agents' motion routes.We have processed a tonal image for determining the contours of objects using a method based on the ant algorithm. In order to reduce the number of "junk" objects, the main principles and stages of the method for multi-scale processing of aerospace images based on the ant algorithm have been outlined. Determining the contours on images with a different value of the scale factor is carried out applying a method based on the ant algorithm. In addition, we rescale images with a different scale factor value to the original size and calculate the image filter. The resulting image is a pixelwise product of the original image and the image filter.The multiscale processing of tonal aerospace images with different scale values has been performed using methods based on the ant algorithms. It was established that application of a multi-scale processing reduces the number of "junk" objects. At the same time, due to multi-scale processing, not the objects' contours are determined but the objects in full.We estimated errors of first and second kind in determining the contours of objects on tonal aerospace images based on the ant algorithms. It was established that using the constructed methods has made it possible to reduce the first and second kind errors in determining the contours on tonal aerospace images by the magnitude of 18–22 % on averag

Improved evolution method of segmentation of multivally scale sequence of images received from the board of space optic-electronic observation systems

Проаналізовані відомі методи сегментування багатомасштабної послідовності оптико-електронних зображень. Встановлено, що відомі методи сегментування не можуть бути використані при сегментуванні зображень, отриманих з космічних систем оптико-електронного спостереження. Запропоновано при сегментуванні таких зображень використовувати еволюційний метод сегментування. Однак при цьому на сегментованому зображенні з’являється велика кількість об’єктів невеликого розміру, які не дозволяють проводити подальше дешифрування зображення з необхідною якістю. Запропоновано удосконалений метод сегментування багатомасштабної послідовності зображень, отриманих з космічних систем оптикоелектронного спостереження. Отримано зображення-фільтр, використання якого дозволило знизити велику кількість об’єктів невеликого розміру, а об’єкти, що мають ознаки для дешифрування, виділяються з необхідною якістю.Проанализированы известные методы сегментации многомасштабной последовательности оптико-электронных изображений. Установлено, что известные метолы сегментации не могут быть использованы при сегментации изображений, полученных с космических систем оптико-электронного наблюдения. Предложено при сегментации таких изображений использовать эволюционный метод сегментации. Однако, при этом на сегментированном изображении появляется большое число объектов малого размера, которые не позволяют проводить дальнейшее дешифрирование изображения с требуемым качеством. Предложен усовершенствованный метод сегментации многомасштабной последовательности изображений, полученных с бортовых систем оптико-электронного наблюдения. Получено изображение-фильтр, использование которого позволило снизить большое количество объектов малого размера, а объекты, которые имеют признаки для дешифрирования, выделяются с необходимым качеством.The known methods of segmentation of a multiscale sequence of optoelectronic images are analyzed. It is established that the known segmentation methods can not be used in the segmentation of images obtained from space systems of optic-electronic observation. It has been proposed to use the evolutionary method of segmentation when segmenting such images. However, at the same time a large number of small objects appear on the segmented image, which does not allow further decoding of the image with the required quality. An improved method of segmentation of multiscale sequence of images obtained from on-board opticelectronic surveillance systems is proposed. Obtained image-filter, the use of which allowed to reduce a large number of small objects, and objects that have signs for decoding, are allocated with the required quality

Information technology of segmentation of images received from on-board optical-electronic observation systems

Запропонована інформаційна технологія сегментування зображення, що отримано з бортових систем оптико-електронного спостереження. В основі технології покладено еволюційні методи сегментування та методи сегментування багатомасштабної послідовності оптико-електронних зображень еволюційним методом.Предложена информационная технология сегментации изображений, полученных с бортовых систем оптико-электронного наблюдения. В основу технологии положены эволюционные методы сегментации и методы сегментации многомасштабной последовательности оптико-электронных изображений эволюционным методом.The information technology of segmentation of images obtained from on-board optical-electronic surveillance systems is proposed. The technology is based on evolutionary methods of segmentation and methods of segmentation of a multiscale sequence of optoelectronic images by an evolutionary method

Developing the Model of Reliability of A Complex Technical System of Repeated Use with A Complex Operating Mode

Solving the problems of setting requirements to the reliability of complex technical systems for various purposes presupposes their classification according to the features characterizing the purpose, modes of use, etc. According to the modes of use, systems are divided into objects of continuous long-term use, repeated cyclic use, and single-use. The objects of repeated cyclic use include the systems operating in cycles. Durations of the periods of work and pause in the cycle are considered deterministic values. Technological and/or technical maintenance is carried out in pauses between the operation periods.In addition to the known classification, it was proposed to introduce a group of systems of repeated use with a complex operating mode. A complex mode is understood as a mode that includes waiting for a request of the system use and executing the request after it arrives at a random time.An analytical model of reliability of such a system has been developed in the form of a ratio for a non-stationary total coefficient of operational readiness. This model describes the processes of the system functioning in the intervals of waiting and use. In this case, the duration of the intervals of waiting and/or execution of the request are random values.Ratios for this indicator were obtained for three options of specifying the functions of distribution of durations of waiting in a turn-on condition and fulfilling the request for use.The developed model makes it possible to set requirements for reliability and maintainability of the systems with a complex operating mode.The results of modeling the dependences of the operational indicators of reliability on parameters of the functions of distribution of durations of waiting and executing the request were obtained for different distributions. Recommendations were formulated concerning the substantiation of the requirements to reliability and maintainability of the systems under consideratio