DOI: https://doi.org/10.32515/2664-262X.2022.5(36).2.335-346
Neural Network Method for Detecting Textural Anomalies in a Digital Image
About the Authors
Oleksandr Drieiev, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, e-mail: drieievom@kntu.kr.ua, ORCID ID: 0000-0001-6951-2002
Oleksandr Dorenskyi, PhD in Technics (Candidate of Technics Sciences), Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, e-mail: dorenskyiop@kntu.kr.ua, ORCID ID: 0000-0002-7625-9022
Hanna Drieieva, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, e-mail: gannadreeva@gmail, ORCID ID: 0000-0002-8557-3443
Dmytro Holub, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, e-mail: dimchik529@gmail.com, ORCID ID: 0000-0003-4984-1161
Abstract
Modern computer vision systems often use neural networks to process images. But to use neural networks, you need to create databases to train these neural networks. In some cases, creating a training database takes the vast majority of the project's financial and human resources. Therefore, the actual task of finding methods to improve the quality of learning neural networks on small data is considered in this article. The ability to process data, which nature was not present in the original training database is relevant, also. To solve the problem of improving the quality of image segmentation by textural anomalies, this research is proposed to use as input to the neural network not only the image but also its local statistic data. It can increase the information content of the input information for the neural network. Therefore, neural networks do not need to learn to choose statistical features but simply use them.
This investigation classifies the requirements for image segmentation systems to indicate atypical texture anomalies. The literature analysis revealed various methods and algorithms for solving such problems. As a result, in this science work, the process of finding features in the photo is summarized in stages. The division into stages of search for features allowed to choose arguments for methods and algorithms that can perform the task. At each stage, requirements were formed for methods, that allowed separate the transformation of image fragments into a vector of features by using an artificial neural network (trained on a separate image of the autoencoder). Statistical features supplement by the vector of features of the image fragment.
Numerous experiments have shown that the generated feature vectors improve the classification result for an artificial Kohonen neural network, which is able to detect atypical image fragments.
Keywords
image segmentation, neural network, Kohonen, autoencoder, convolution, Kohonen's neural networks
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References
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GOST Style Citations
Diwakar Tripathia, Damodar Reddy Edlaa, Venkatanareshbabu Kuppilia, Annushree Bablania, Ramesh Dharavath . Credit Scoring Model based on Weighted Voting and Cluster based Feature Selection. International Conference on Computational Intelligence and Data Science (ICCIDS 2018), Procedia Computer Science 132 (2018) 22–31. https://www.sciencedirect.com/science/article/ pii/S1877050918307877 (Last accessed: 12.04.2022)
Hong-an Li, Qiaoxue Zheng, Xin Qi, Wenjing Yan, Zheng Wen, Na Li, Chu Tang. Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems. Computational Intelligence and Neuroscience, vol. 2021, Article ID 8387382, 11 pages, 2021. https://doi.org/10.1155/2021/8387382
Vadym Slyusar, Mykhailo Protsenko and other. Improving neural network model for semantic segmentation of images of monitored objects in aerial photographs. Eastern-European Journal of Enterprise Technologies. ISSN 1729-3774, DOI: 10.15587/1729-4061.2021.248390. P. 86-95. https://slyusar.kiev.ua/Article%20Text-573693-1-10-20211229.pdf
Potapov A.A., German V.A., Grachev V. I. "“Nano” and radar signal processing: Fractal reconstruction complicated images, signals and radar backgrounds based on fractal labyrinths. 2013 14th International Radar Symposium (IRS), 2013, pp. 941-946. https://ieeexplore.ieee.org/abstract/document/6581701 (Last accessed: 15.04.2022)
Qiang Zuo, Songyu Chen, Zhifang Wang. R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation. Security and Communication Networks. Vol. 2021, Article ID 6625688, 10 pages, 2021. https://doi.org/10.1155/2021/6625688
Wang Shuhang, Hu Szu-Yeu, Cheah Eugene, Wang Xiaohong and other. U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation. https://arxiv.org/abs/2004.03466 (Last accessed: 15.04.2022)
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Tang, Peng, Chunyu Wang, Xinggang Wang, Wenyu Liu, Wenjun Zeng and Jingdong Wang. Object Detection in Videos by High Quality Object Linking. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (2020): 1272-1278. https://ieeexplore.ieee.org/document/8686124
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Copyright (c) 2022 Oleksandr Drieiev, Oleksandr Dorenskyi, Hanna Drieieva
Neural Network Method for Detecting Textural Anomalies in a Digital Image
About the Authors
Oleksandr Drieiev, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, e-mail: drieievom@kntu.kr.ua, ORCID ID: 0000-0001-6951-2002
Oleksandr Dorenskyi, PhD in Technics (Candidate of Technics Sciences), Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, e-mail: dorenskyiop@kntu.kr.ua, ORCID ID: 0000-0002-7625-9022
Hanna Drieieva, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, e-mail: gannadreeva@gmail, ORCID ID: 0000-0002-8557-3443
Dmytro Holub, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, e-mail: dimchik529@gmail.com, ORCID ID: 0000-0003-4984-1161
Abstract
Keywords
Full Text:
PDFReferences
1. Diwakar Tripathia, Damodar Reddy Edlaa, Venkatanareshbabu Kuppilia, Annushree Bablania, Ramesh Dharavath (2018). Credit Scoring Model based on Weighted Voting and Cluster based Feature Selection. International Conference on Computational Intelligence and Data Science (ICCIDS 2018), Procedia Computer Science 132 22–31. https://www.sciencedirect.com/science/article/pii/S1877050918307877 [in English].
2. Hong-an Li, Qiaoxue Zheng, Xin Qi, Wenjing Yan, Zheng Wen, Na Li, Chu Tang. (2021). Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems. Computational Intelligence and Neuroscience, vol. 2021, Article ID 8387382, 11 pages. https://doi.org/10.1155/2021/8387382 [in English].
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5. Qiang Zuo, Songyu Chen, Zhifang Wang. (2021). R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation", Security and Communication Networks, vol. 2021, Article ID 6625688, 10 pages, https://doi.org/10.1155/2021/6625688 [in English].
6. Wang Shuhang, Hu Szu-Yeu, Cheah Eugene, Wang Xiaohong and other (2004). U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation. https://arxiv.org/abs/2004.03466 [in English].
7. Veys, C., Chatziavgerinos, F., AlSuwaidi, A. et al. (2019). Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape. Plant Methods, 15, 4 https://doi.org/10.1186/s13007-019-0389-9 [in English].
8. Tang, Peng, Chunyu Wang, Xinggang Wang, Wenyu Liu, Wenjun Zeng & Jingdong Wang. (2020). Object Detection in Videos by High Quality Object Linking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42: 1272-1278. https://ieeexplore.ieee.org/document/8686124 [in English].
9. Fasola, J. & Veloso, M. (2006). Real-time object detection using segmented and grayscale images. Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006., pp. 4088-4093, doi: 10.1109/ROBOT.2006.1642330. https://ieeexplore.ieee.org/abstract/document/1642330 [in English].
10. Abuarafah Adnan, Khozium Osama, Abdrabou Essam. (2012). Real-time Crowd Monitoring using Infrared Thermal Video Sequences. International Journal of Engineering Science. 8. 133-140. https://www.researchgate.net/publication/236292403_Real-time_Crowd_Monitoring_using_Infrared_Thermal_Video_Sequences [in English].
11. Haralick, R.M., Shanmugam, K. & Dinstein, I. (1973). Textural Features for Image Classification, in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610-621, Nov. doi: 10.1109/TSMC.1973.4309314 [in English].
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18. Marimont, R.B. & Shapiro, M.B. (1979). Nearest Neighbour Searches and the Curse of Dimensionality. IMA Journal of Applied Mathematics, Vol. 24, Issue 1, August, Pp. 59–70, https://doi.org/10.1093/imamat/24.1.59 [in English].
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20. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69 [in English].
21. Ojie, Oseikhuemen D., & Reza Saatchi. (2021). Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive and Vestibular Systems to Balance in Young Healthy Adult Subjects. Healthcare 9, no. 9: 1219. https://doi.org/10.3390/healthcare9091219 [in English].