Construction of methods for determining the contours of objects on tonal aerospace images based on the ant algorithms
DOI:
https://doi.org/10.15587/1729-4061.2019.177817Keywords:
aerospace image, objects' contours, ant algorithm, multiscale processing, image-filterAbstract
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 averageReferences
- Weng, Q. (2009). Remote Sensing and GIS Integration. New York: McGraw-Hill Professional, 416.
- Chemin, Y. (Ed.) (2012). Remote Sensing of Planet Earth. Rijeka, 250. doi: https://doi.org/10.5772/2291
- Richards, J. (2013). Remote Sensing Digital Image Analysis. An Introduction. Springer. doi: https://doi.org/10.1007/978-3-642-30062-2
- Vysotska, V., Lytvyn, V., Burov, Y., Gozhyj, A., Makara, S. (2018). The consolidated information web-resource about pharmacy networks in city. CEUR Workshop Proceedings (Computational linguistics and intelligent systems), 2255, 239–255.
- Stryzhak, O., Prychodniuk, V., Podlipaiev, V. (2019). Model of Transdisciplinary Representation of GEOspatial Information. Advances in Information and Communication Technologies, 34–75. doi: https://doi.org/10.1007/978-3-030-16770-7_3
- Lytvyn, V., Vysotska, V. (2015). Designing architecture of electronic content commerce system. 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT). doi: https://doi.org/10.1109/stc-csit.2015.7325446
- Karamti, H., Tmar, M., Gargouri, F. (2017). A new vector space model for image retrieval. Procedia Computer Science, 112, 771–779. doi: https://doi.org/10.1016/j.procs.2017.08.202
- Gonzalez R. C., Woods R. E. (2017). Digital Image Processing. Prentice Hall, 1192.
- Gupta, V., Singh, D., Sharma, P. (2016). Image Segmentation Using Various Edge Detection Operators: A Comparative Study. International Journal of Innovative Research in Computer and Communication Engineering, 4 (8), 14819–14824.
- Kabade, A., Sangam, V. (2016). Canny edge detection algorithm. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), 5 (5), 1292–1295.
- Yang, M., Chao, H., Zhang, C., Guo, J., Yuan, L., Sun, J. (2016). Effective Clipart Image Vectorization through Direct Optimization of Bezigons. IEEE Transactions on Visualization and Computer Graphics, 22 (2), 1063–1075. doi: https://doi.org/10.1109/tvcg.2015.2440273
- Sum, K., S. Cheung, P. (2006). A Fast Parametric Snake Model with Enhanced Concave Object Extraction Capability. 2006 IEEE International Symposium on Signal Processing and Information Technology. doi: https://doi.org/10.1109/isspit.2006.270844
- Karamti, H., Tmar, M., Gargouri, F. (2014). Vectorization of Content-based Image Retrieval Process Using Neural Network. Proceedings of the 16th International Conference on Enterprise Information Systems, 435–439. doi: https://doi.org/10.5220/0004972004350439
- Nyandwi, E., Koeva, M., Kohli, D., Bennett, R. (2019). Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction. Remote Sens, 11, 1662. doi: https://doi.org/10.20944/preprints201905.0342.v1
- Ramlau, R., Scherzer, O. (Eds.) (2019). The Radon Transform. Berlin/Boston: Walter de Gruyter GmbH. doi: https://doi.org/10.1515/9783110560855
- Li, Z., Liu, Y., Walker, R., Hayward, R., Zhang, J. (2009). Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Machine Vision and Applications, 21 (5), 677–686. doi: https://doi.org/10.1007/s00138-009-0206-y
- Manzanera, A., Nguyen, T. P., Xu, X. (2016). Line and circle detection using dense one-to-one Hough transforms on greyscale images. EURASIP Journal on Image and Video Processing, 2016 (1). doi: https://doi.org/10.1186/s13640-016-0149-y
- El-Baz, A., Jiang, X., Jasjit, S. (Eds.) (2016). Biomedical image segmentation: advances and trends. CRC Press, 546. doi: https://doi.org/10.4324/9781315372273
- Ruban, I., Khudov, V., Makoveichuk, O., Khudov, H., Khizhnyak, I. (2018). A Swarm Method for Segmentation of Images Obtained from On-Board Optoelectronic Surveillance Systems. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). doi: https://doi.org/10.1109/infocommst.2018.8632045
- Ruban, I., Khudov, H., Makoveichuk, O., Khizhnyak, I., Khudov, V., Podlipaiev, V. et. al. (2019). Segmentation of optical-electronic images from on-board systems of remote sensing of the earth by the artificial bee colony method. Eastern-European Journal of Enterprise Technologies, 2 (9 (98)), 37–45. doi: https://doi.org/10.15587/1729-4061.2019.161860
- 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, 5 (9 (89)), 49–57. doi: https://doi.org/10.15587/1729-4061.2017.109904
- Dorigo, M., Stützle, T. (2018). Ant Colony Optimization: Overview and Recent Advances. International Series in Operations Research & Management Science, 311–351. doi: https://doi.org/10.1007/978-3-319-91086-4_10
- WorldView-1 Satellite Sensor. Satellite Imaging Corporation. Available at: http://www.satimagingcorp.com/satellite-sensors/worldview-1
- Ruban, I., Khudov, V., Khudov, H., Khizhnyak, I. (2017). An improved method for segmentation of a multiscale sequence of optoelectronic images. 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). doi: https://doi.org/10.1109/infocommst.2017.8246367
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Mykola Chomik, Vladyslav Khudov, Irina Khizhnyak, Viacheslav Podlipaiev, Yurii Sheviakov, Oleksii Baranik, Artem Irkha
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.