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J. Imaging
Volume 12
Issue 7
10.3390/jimaging12070326
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Article

Microscopic Pollen Image Classification via Contour-Signal Representation, Wavelet Analysis, and CNN

by
Abror Shavkatovich Buriboev
1,
Akhram Nishanov
2,
Shuxrat Isroilov
3,
Inomjon Narzullaev
2,
Umidjon Djumayozov
3,
Shavkat Buriboyev
4,
Temur Azamov
5,
Parda Yuldashov
6,
Davron Shodmonov
3,
Djamshid Sultanov
2,* and
Abbos Abduvaytov
7,*
1
Department of Exact Sciences, Kimyo International University in Tashkent, Tashkent 100121, Uzbekistan
2
Department of Software of Information Systems, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent 100084, Uzbekistan
3
Department of Computing Systems Engineering, Samarkand State University, Samarkand 140104, Uzbekistan
4
Department of Civil Engineering, Samarkand State Technical University, Samarkand 140143, Uzbekistan
5
Agency Innovative Development, Tashkent 100174, Uzbekistan
6
Department of Surgery, Samarkand State Medical University, Samarkand 140100, Uzbekistan
7
Department of Computer Engineering, Samarkand Institute of Economics and Service, Samarkand 140100, Uzbekistan
*
Authors to whom correspondence should be addressed.
J. Imaging 2026, 12(7), 326; https://doi.org/10.3390/jimaging12070326
Submission received: 6 June 2026 / Revised: 11 July 2026 / Accepted: 15 July 2026 / Published: 18 July 2026
(This article belongs to the Section Computer Vision and Pattern Recognition)
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Abstract

Accurate classification of pollen grains in microscopic images remains challenging because of noise, structural variability, background complexity, weak texture, and intra-class similarity. To address these issues, this study proposes a hybrid framework that integrates contour-signal modeling, spectral–wavelet analysis, and deep learning for robust microscopic pollen image recognition. In the proposed approach, microscopic pollen images are first converted into contour-based point-signal representations, allowing object boundaries to be analyzed as structured one-dimensional signals. To improve signal quality under real imaging conditions, the framework incorporates Gaussian, median, and contour-aware filtering together with defect-point detection and correction. The processed contour signals are then analyzed using Fourier transform, continuous wavelet transform, and discrete wavelet transform to extract complementary global and local descriptors. These enriched representations are provided to a convolutional neural network for final classification. Experiments conducted on a seven-class microscopic pollen-image dataset demonstrate that the proposed method outperforms conventional computer-vision and baseline deep-learning approaches. The best-performing hybrid configuration achieved an error rate of 6.4%, while the overall classification accuracy reached 0.977 with an F1-score of 0.966, compared with 0.837 for a traditional computer-vision pipeline. These results confirm that combining contour-based signal processing with hierarchical deep feature learning provides an effective and noise-robust strategy for microscopic pollen image recognition. However, the present validation is limited to pollen images, and further experiments on broader microscopic object datasets are required to assess generalization to other micro-object categories such as nanoparticles, fibers, rods, and synthetic microstructures.
Keywords: micro-object recognition; computer vision; wavelet transform; Fourier transform; convolutional neural network; image segmentation; hybrid models micro-object recognition; computer vision; wavelet transform; Fourier transform; convolutional neural network; image segmentation; hybrid models

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MDPI and ACS Style

Buriboev, A.S.; Nishanov, A.; Isroilov, S.; Narzullaev, I.; Djumayozov, U.; Buriboyev, S.; Azamov, T.; Yuldashov, P.; Shodmonov, D.; Sultanov, D.; et al. Microscopic Pollen Image Classification via Contour-Signal Representation, Wavelet Analysis, and CNN. J. Imaging 2026, 12, 326. https://doi.org/10.3390/jimaging12070326

AMA Style

Buriboev AS, Nishanov A, Isroilov S, Narzullaev I, Djumayozov U, Buriboyev S, Azamov T, Yuldashov P, Shodmonov D, Sultanov D, et al. Microscopic Pollen Image Classification via Contour-Signal Representation, Wavelet Analysis, and CNN. Journal of Imaging. 2026; 12(7):326. https://doi.org/10.3390/jimaging12070326

Chicago/Turabian Style

Buriboev, Abror Shavkatovich, Akhram Nishanov, Shuxrat Isroilov, Inomjon Narzullaev, Umidjon Djumayozov, Shavkat Buriboyev, Temur Azamov, Parda Yuldashov, Davron Shodmonov, Djamshid Sultanov, and et al. 2026. "Microscopic Pollen Image Classification via Contour-Signal Representation, Wavelet Analysis, and CNN" Journal of Imaging 12, no. 7: 326. https://doi.org/10.3390/jimaging12070326

APA Style

Buriboev, A. S., Nishanov, A., Isroilov, S., Narzullaev, I., Djumayozov, U., Buriboyev, S., Azamov, T., Yuldashov, P., Shodmonov, D., Sultanov, D., & Abduvaytov, A. (2026). Microscopic Pollen Image Classification via Contour-Signal Representation, Wavelet Analysis, and CNN. Journal of Imaging, 12(7), 326. https://doi.org/10.3390/jimaging12070326

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