Abstract
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 made
Highlights
The experience of recent armed conflicts, hybrid wars and anti-terrorist operation on the territory of Donetsk and Lugansk regions of Ukraine shows that the main means of reconnaissance are unmanned aerial vehicles (UAVs) and space surveillance systems
Discussion of the results of application of the evolutionary method to segmentation of optoelectronic images The paper considers a possibility of the application of the simpler and more advanced evolutionary methods to segmentation of the images obtained from the onboard optoelectronic surveillance systems
The process of image segmentation was presented as a set of areas of movement of agents
Summary
The experience of recent armed conflicts, hybrid wars and anti-terrorist operation on the territory of Donetsk and Lugansk regions of Ukraine shows that the main means of reconnaissance are unmanned aerial vehicles (UAVs) and space surveillance systems. Continuous improvement of both specialized survey equipment and its carriers and, rise in information flows [7,8,9,10] from onboard systems of optoelectronic surveillance is observed, The process of obtaining optoelectronic images is accompanied by the influence of such negative factors as motion and vibration of the survey apparatus, non-standardized lighting conditions, various kinds of distortions, interference with data transmission, etc. The urgency of development of an efficient segmentation method for images obtained from onboard systems of optoelectronic surveillance is determined by [15,16,17]: – improvement of special mathematical and software support for automation of the most complex steps of surveillance information processing; – cutting processing time and enhancing reliability of the resulting documents; – intellectualization of data processing (detailed surveillance of the objects of interest, solution of large-scale topical tasks for big territories)
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