№1, 2024
CRITERIA FOR ASSESSING THE ADEQUACY OF IMAGE RECOGNITION METHODS AND THEIR VERIFICATION USING EXAMPLES OF ARTIFICIAL SERIES OF SIGNALS
The article discusses four criteria for assessing the adequacy of the most well-known image recognition methods. Verification of two of these criteria is carried out by empirical analysis using the example of the most well-known signal recognition methods, such as DTW, DDTW, as well as methods based on the Wavelet transform and Fourier transform. Two artificial sets of images are used as recognition objects, formed by uniformly shifting the base image both horizontally and vertically. In general, the goal of this research is to develop a new method for extracting recognition features using the example of the image of the State Emblem of the Republic of Azerbaijan. In the context of this study, a verification of a previously proposed signal recognition algorithm is carried out based on the artificial family of curves, for which the most accessible and acceptable method of displacement is established: horizontally or simultaneously horizontally and vertically (pp.10-17).
Keywords:Image, One-dimensional signal, Artificial family of curves, Recognition feature, Segmentation, Recognition method
References
- Basha, Sh.H., Jaison B. (2022). A novel secured Euclidean space points algorithm for blind spatial image watermarking. EURASIP Journal on Image and Video Processing.
https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-022-00590-w - Cedro, L., Janecki, D. (2011). Determining of signal derivatives in identification problems. FIR Differential Filters, Acta Montanistica Slovaca Rocník, 16(1), 47–54.
- Cote, M., Dash, A., Branzan, Albu A. (2023). Semantic segmentation of textured mosaics. EURASIP Journal on Image and Video Processing.
https://jivp-eurasipjournals.springeropen.com/articles/ 10.1186/s13640-023-00613-0 - Hindarto, H., Anshory, I., Efiyanti, A. (2017). Feature extraction of heart signals using fast Fourier transform, UNEJ e-Proceeding 165-167.
https://jurnal.unej.ac.id/index.php/prosiding/article/view/4187 - Keogh, E., Pazzani, M. (2017). Derivative dynamic time warping. In Proceedings, 2001th SIAM International Conference on Data Mining (SDM), https://doi.org/10.1137/1.9781611972719.1
- Kerimov, A. (2022). Accuracy comparison of signal recognition methods on the example of a family of successively horizontally displaced curves. Informatics and Control Problems, 42(2), 80–91.
- Kerimov, A. (2022). Comparison of some signal recognition methods for their adequacy. In Proceedings, 8th International Conference on Control and Optimization with Industrial Applications. 1, Baku, Azerbaijan.
- Kumar, G., Bhatia, P. (2014). Detailed review of feature extraction in image processing systems. In PROCEEDINGS, 4th International Conference on Advanced Computing and Communication Technologies. Rohtak, Haryana, India.
- Liu, Y., Lin, J. (2019). A general-purpose signal processing algorithm for biological profiles using only first-order derivative information. BMC Bioinformatics, 20, 611. https://doi.org/10.1186/s12859-019-3188-4
- Novozhilov, B.M. (2016). Calculation of the derivative of an analog signal in a programmable logic controller. Aerospace Scientific Journal of the Moscow State Technical University N.E. Bauman, 1–12 (In Russian).
- Rzayev, R., Kerimov, A. (2023). Comparison of signal recognition methods by combined use of appropriate evaluation criteria within the additive convolution. Problems of Information Society, 14 (2), 24–31.
- Rzayev, R., Kerimov, A. (2023). Signal recognition by using addictive convolution criteria. Proceedings of IAM, 12(1), 52–64.
- Rzayev, R., Kerimov, A. (2023). Signal recognition using weighted additive convolution of evaluation criteria. The Springer Series “Lecture Notes in Net-works and Systems”, 758(2), 407–416.
- Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1). https://ieeexplore.ieee.org/document/1163055
- Santos, M., Morais, C., Nascimento, Y., Araujo, J., Lima, K. (2017). Spectroscopy with computational analysis in biological studies: a decade (2006–2016). TrAC Trends Anal Chem, 97, 244–256.
- Saraswat, S., Srivastava, G., Sachchidanand, N. (2017). Wavelet transform based feature extraction and classification of atrial fibrillation arrhythmia. Biomed Pharmakoi Journal 10(4) http://biomedpharmajournal.org/?p=17470
- Singh, A. (2023). Beginner-Friendly Techniques to Extract Features from Image Data using Python.
https://www.analyticsvidhya.com/blog/2019/08/3-techniques-extract-features-from-image-data-machine-learning-python/ - Sui, K. (2023). Correction: Research on application of multimedia image processing technology based on wavelet transform. EURASIP Journal on Image and Video Processing. DOI:10.1186/s13640-023-00603-2
- Song, T., Yu, X., Yu, Sh., Ren, Z., Qu, Y. (2021). Feature extraction processing method of medical image fusion based on neural network algorithm. Hindawi, Complexity.
https://doi.org/10.1155/2021/7523513 - Zhao, M., Chai, Q., Zhang, Sh. (2009). A method of image feature extraction using wavelet. In Proceedings, International Conference on Intelligent Computing, ICIC, Emerging Intelligent Computing Technology and Applications, 187–192.
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