Опубликован специальный выпуск журнала Pattern Recognition and Image Analysis (Vol. 36, No. 2, 2026), посвященный 22-й Всероссийской конференции с международным участием «Математические методы распознавания образов» (ММРО-2025).

PRIA Journal Special Issue “22nd All-Russian Conference with International Participation “Mathematical Methods of Pattern Recognition” (MMPR-2025), September 22–26, 2025, Murom, Russian Federation. Selected Papers”

• A. A. Dokukin, I. A. Matveev, and O. V. Senko. 22nd All-Russian Conference with International Participation “Mathematical Methods of Pattern Recognition” (MMPR-2025), September 22–26, 2025, Murom, Russian Federation, p. 634.

Plenary Papers

• A. I. Chulichkov, A. S. Khristova. Nonlinear Estimates of the Maximum Possibility in the Theory of Computer-Aided Measuring Transducers, p. 634.
• V. M. Nedel’ko. Some Expressions for the Variance of a Cross-Validation, p. 642.

Section Papers—Theoretical Advances

• I. Yu. Torshin. Theoretical Interrelation between the Concepts of Problem Regularity and Topological Space Normality, and Practical Applications of the Developed Theory in Data Mining, p. 648.
• A. P. Khalov, O. M. Ataeva, N. P. Tuchkova. Creating a Multimodal Dataset for the SciLibRu Semantic Library Using a Language Model, p. 665.
• O. V. Senko, P. Y. Krivulia. New Schemes for Constructing Ensembles Based on Divergent Forest, p. 678.
• Yu. O. Kuznetsova, N. N. Kiselyova, O. V. Senko, A. A. Dokukin. Using High-Divergence Ensembles in Predicting Quantitative Properties of Inorganic Compounds, p. 689.
• Z. M. Shibzukhov. On One Robust Variant of Boosting Classifiers, p. 699.
• A. V. Grabovoy. Landscape Measure of Deep Learning Models, p. 708.
• A. A. Orlov, E. S. Abramova. Algorithm for Shift-Based Incremental Learning of a Neural Network under Continuous Data Inflow Conditions, p. 717.
• D. V. Anisimova, E. V. Djukova, I. A. Pestov. Logical Data Analysis and Classification: Traditional and Neural Network Approaches, p. 729.

Section Papers—Applications in Image Processing

• M. M. Lange, A. M. Lange, S. V. Paramonov. Information-Theoretic Approach to Analysis of Personal Identification Fidelity Using Multimodal Datasets, p. 738.
• A. K. Sokolov, M. Y. Nikitin. Cooperative Face Liveness Detection via Displacement of Dense Facial Points Clouds, p. 747.
• L. M. Mestetskiy, Mingchuan Xu. Constellation Method for One-Shot Search of Handwritten Glyphs, p. 756.
• D. M. Murashov. On Assessing the Boundaries of the Information-Theoretical Measures of Digital Image Segmentation Quality, p. 764.
• V. S. Pavlova, M. Yu. Kurbakov, A. V. Kopylov, O. S. Seredin. Segmentation of Microscopic Images of Pseudomonas Biofilms Using Deep Learning and Semantic Filtering, p. 777.
• K. I. Kiy. Real-Time Image Segmentation and Its Application to Scene Analysis, p. 793.
• A. V. Abakumov, S. V. Eremeev. Image Segmentation Based on Topological Decomposition and Pixel Resource, p. 807.
• I. F. Serzhenko, A. N. Gneushev. Training Normalizing Flow Models in the Latent Space of Variational Autoencoders for Image Entropy Compression, p. 817.
• A. B. Murynin, A. K. Shved, V. A. Kozub. Neural Network Models for Improving Satellite Image Quality Based on Pixel-Level Domain Adaptation, p. 829.
• E. O. Zubova, M. N. Shamshin, A. V. Rybakov. Determining the Offset of a Metal Strip Using Computer Vision in the Forming Mill Line during the Production of Electric-Welded Pipes, p. 842.

Section Papers—Signal Analysis and Information Retrieval

• M. M. Lange. Boundary Relations between Amount of Information and Decision Fidelity for Data Coding and Analysis Models, p. 328.
• A. V. Astafiev. Neural Network Algorithm for Recognizing Human Activity from Radio Signals with Preliminary Motion Detection, p. 852.
• T. V. Yakovleva. Estimating the Parameters of a Harmonic Signal Distorted by Gaussian Noise Using Statistical Phase Analysis: Existence and Uniqueness of Solution, p. 863.
• V. Ya. Chuchupal, S. I. Gurov. Assessment of Pronunciation Correctness Using Large Multilingual Models, p. 872.
• A. K. Zvereva, A. V. Grabovoi, M. S. Kaprielova. Structure-Oriented Augmentation for Improving the Generalization Ability of Neural Pattern Recognition Models under Data Scarcity, p. 882.