IMAGE PROCESSING MODELS AND METHODS RESEARCH AND WAYS OF IMPROVING MARKER RECOGNITION TECHNOLOGIES IN ADDED REALITY SYSTEMS
AbstractThe subject matter of article is method of image processing, which identify and describe the local features of images. The aim of the article is the determination of ways for interconnection of the methods for processing the image and technologies creation in the development of markers in the systems of additional reality. The following tasks are solved in the article: to analyze the existing methods and algorithms for finding objects in two-dimensional images to determine the basic marker recognition technology in the complementary reality systems. Analyzed genetic, neural network, statistical and fractal methods, as well as approaches to the algorithms implementation of in the software construction for systems of complementary reality. The next results were obtained: a review and a comparative analysis of the main known algorithms for detecting key points in the images were conducted. It was suggested in the development of marker recognition methods it is necessary to develop a procedure of preliminary image processing for the formation algorithms of the front image for the marker under different conditions of obtaining images. At segmentation stages, it is expedient to use genetic algorithms based on the best indicators of proper segmentation and low processing time, but it is necessary to develop functions that are appropriate for the format of the markers. Improve existing methods for processing segmentation results based on a criterion base describing a visual model representing a marker. Conclusions: as a result of the analysis, the following conclusion can be drawn. The fastest and the most accurate algorithm for putting key points is the genetic algorithm (average time of the algorithm is 5.23 seconds, the number of correct answers is 84.25). The longest working time is the neural network method - 8.45 seconds, the accuracy of this algorithm is also the lowest - 52. Another advantage of the algorithm of point matching is that if the object goes beyond the frame and then returns again, the program will again continue to track this object. This is supported by algorithms of machine learning. You can also notice that the SIFT calculation works much faster than fractal texture analysis. These results suggest that there are currently no methods for recognizing markers, allowing high accuracy of less than one unit to recognize in a short time. In our opinion, one of the promising directions is the use of Royan methods, namely the development of target functions for accurate and fast recognition of the image by markers.
Lyamov, Yu. (2014), "Augmented reality technology" ["Tekhnologiya dopolnennoy real'nosti"], Modern technology and technology, No. 9, available at: http://technology.snauka.ru/2014/09/4567 (last accessed 23.11.2018).
Samoilov, A. N, Shevchenko, I. V. (2015), "Methods of reconstructing the contour line of the dislocation of a digital image of Arseny Gaul's plate" ["Metody vosstanovlenija linii kontura dislokacii cifrovogo izobrazhenija plastiny Arsenida Gallija"], Eastern-European Journal of Enterprise Technologies, Vol. 3, No. 5 (75), P. 9.
Ruban, I. V., Shitova, O. V., Vel'chev, K. A. (2008), "The method of localization of objects in the systems of image processing of remote sensing of the eart" ["Metod lokalizatsii ob"ektov v sistemakh obrabotki izobrazheniy distantsionnogo zondirovaniya zemli"], Management, navigation and communication systems, No. 1 (5), P. 35–37.
Krig, S. (2016), "Interest Point Detector and Feature Descriptor Survey", Computer Vision Metrics, P. 187–246.
Mikolajczyk, K., Schmid, C. (2005), "A performance evaluation of local descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, No. 27 (10), P. 1615–1630.
Beham, A., Wagner, S., Winkler, S., Affenzeller, M. (2017), "Genetic Algorithms", Encyclopedia of Computer Science and Technology, Vol. 2, P. 469–480.
Kass, M., Witkin, A., Terzopoulos, D. (1988), "Snakes: Active contour models", International journal of computer vision, Vol. 1, No. 4, P. 321–331.
Kenneth, A De Jong (2006), "Evolutionary computation: a unified approach", MIT press, P. 64–65.
Bodyanskiy, Y., Vynokurova, O., Pliss, I., Setlak, G., Mulesa, P. (2016), "Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks", IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv (Ukraine), P. 257–262.
Kobayashi, M. (2017), "Fixed points of split quaternionic hopfield neural networks", Signal Processing, Vol. 136, P. 38–42.
Bodyanskiy, Y. V., Tyshchenko, A. K. , Deineko, A. A. (2015), "An evolving radial basis neural network with adaptive learning of its parameters and architecture", Automatic Control and Computer Sciences,Vol. 49, No. 5, P. 255–260.
Zhernova, P., Bodyanskiy, Y. (2018), "Kernel fuzzy clustering of data streams based on the ensemble of neural networks", Innovative Technologies and Scientific Solutions for Industries, No. 4 (6), P. 42–47. DOI: https://doi.org/10.30837/2522-9818.2018.6.042.
David, G. (2004), "Lowe Distinctive Image Features from Scale-Invariant Keypoints", International Journal of Computer Vision, P. 91–110.
Geusebroek, J., Smeulders Arnold, van de Weijer Joost. (2012), "An Efficient Algorithm for Fractal Analysis of Textures", 25th SIBGRAPI Conference on Graphics, Patterns and Images, P. 39–46.
Ruban, I., Khudov, H., Khudov, V., Khizhnyak, I., Makoveichuk, O. (2017), "Segmentation of the images obtained from onboard optoelectronic surveillance systems by the evolutionary method", Eastern-European Journal of Enterprise Technologies, Vol 5, No. 9 (89), P. 52–54. DOI: https://doi.org/10.15587/1729-4061.2017.109904.
Long, M., Peng, F. (2013), "A Box-Counting Method with Adaptable Box Height for Measuring the Fractal Feature of Images", Radioengineering, P. 208–213.
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