DEVELOPMENT OF METHODS FOR DETERMINING THE CONTOURS OF OBJECTS FOR A COMPLEX STRUCTURED COLOR IMAGE BASED ON THE ANT COLONY OPTIMIZATION ALGORITHM
A method for determining the contours of objects on complexly structured color images based on the ant colony optimization algorithm is proposed. The method for determining the contours of objects of interest in complexly structured color images based on the ant colony optimization algorithm, unlike the known ones, provides for the following. Color channels are highlighted. In each color channel, a brightness channel is allocated. The contours of objects of interest are determined by the method based on the ant colony optimization algorithm. At the end, the transition back to the original color model (the combination of color channels) is carried out.
A typical complex structured color image is processed to determine the contours of objects using the ant colony optimization algorithm. The image is presented in the RGB color space. It is established that objects of interest can be determined on the resulting image. At the same time, the presence of a large number of "garbage" objects on the resulting image is noted. This is a disadvantage of the developed method.
A visual comparison of the application of the developed method and the known methods for determining the contours of objects is carried out. It is established that the developed method improves the accuracy of determining the contours of objects. Errors of the first and second kind are chosen as quantitative indicators of the accuracy of determining the contours of objects in a typical complex structured color image. Errors of the first and second kind are determined by the criterion of maximum likelihood, which follows from the generalized criterion of minimum average risk. The errors of the first and second kind are estimated when determining the contours of objects in a typical complex structured color image using known methods and the developed method. The well-known methods are the Canny, k-means (k=2), k-means (k=3), Random forest methods. It is established that when using the developed method based on the ant colony optimization algorithm, the errors in determining the contours of objects are reduced on average by 5–13 %.
Gonzalez, R., Woods, R. (2017). Digital Image Processing. Prentice Hall, Upper Saddle Rever, 1192.
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, 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
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
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
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.
Carson, C., Thomas, M., Belongie, S., Hellerstein, J. M., Malik, J. (1999). Blobworld: A System for Region-Based Image Indexing and Retrieval. Lecture Notes in Computer Science, 509–517. doi: https://doi.org/10.1007/3-540-48762-x_63
Natsev, A., Rastogi, R., Shim, K. (1999). WALRUS. ACM SIGMOD Record, 28 (2), 395–406. doi: https://doi.org/10.1145/304181.304217
Bartolini, I., Patella, M., Stromei, G. (2011). The windsurf library for the efficient retrieval of multimedia hierarchical data. Proceedings of the International Conference on Signal Processing and Multimedia Applications. doi: https://doi.org/10.5220/0003451701390148
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. Available at: https://arxiv.org/pdf/1602.01913.pdf
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. 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. (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
Faroogue, M. Y., Raeen, M. S. (2014). Latest trends on image segmentation schemes. International journal of advanced research in computer science and software engineering, 4 (10), 792–795.
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N. (2012). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42 (1), 21–57. doi: https://doi.org/10.1007/s10462-012-9328-0
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
Choudhary, R., Gupta, R. (2017). Recent Trends and Techniques in Image Enhancement using Differential Evolution- A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 7 (4), 106–112. doi: https://doi.org/10.23956/ijarcsse/v7i4/0108
Ruban, I., Khudov, H., Makoveichuk, O., Chomik, M., Khudov, V., Khizhnyak, I. et. al. (2019). Construction of methods for determining the contours of objects on tonal aerospace images based on the ant algorithms. Eastern-European Journal of Enterprise Technologies, 5 (9 (101)), 25–34. doi: https://doi.org/10.15587/1729-4061.2019.177817
Gauch, H. (2002). Scientific Method in Practice. Cambridge University Press. doi: https://doi.org/10.1017/cbo9780511815034
Ikonos Satellite Image Gallery. Available at: https://www.satimagingcorp.com/gallery/ikonos/
Pelleg, D., Moore, A. (2000). X-means: Extending k-means with efficient estimation of the number of clusters. Proceeding of the 17th International Conference on Machine Learning. San Francisco, 727734.
Gonzaga, A. (2009). Method to Evaluate the Performance of Edge Detector. The XXII Brazilian Symposium on Computer Graphics and Image Processing, 87–91.
Copyright (c) 2019 Hennadii Khudov, Igor Ruban, Oleksandr Makoveichuk, Hennady Pevtsov, Vladyslav Khudov, Irina Khizhnyak, Sergii Fryz, Viacheslav Podlipaiev, Yurii Polonskyi, Rostyslav Khudov
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.