Special Issues
Table of Content

AI-Driven Image Processing and Pattern Recognition: Advances in Algorithms, Models, and Applications

Submission Deadline: 01 October 2026 View: 1870 Submit to Special Issue

Guest Editor(s)

Dr. Barmak Honarvar Shakibaei Asli

Email: barmak.honarvar@ieee.org

Affiliation: Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield, United Kingdom

Homepage:

Research Interests: digital signal & image processing, pattern recognition

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Summary

Recent advances in artificial intelligence, deep learning, and computational image processing have fundamentally transformed the fields of image analysis and pattern recognition. This Special Issue aims to bring together cutting-edge research on AI-driven image processing and pattern recognition, focusing on algorithmic innovation, model optimisation, and methodological progress. Topics include, but are not limited to: image reconstruction and enhancement, feature extraction and representation learning, object detection and recognition, image segmentation and classification, generative models and synthetic data, multimodal image fusion, and explainable AI in vision systems. This issue will serve as a platform to showcase how AI technologies can improve the accuracy, efficiency, and scalability of imaging systems, promoting comprehensive advances from methodology to application.

Themes:
· Advances in Image Classification and Recognition
· Algorithmic Optimisation for Image and Instance Segmentation
· Generative Models for Image Enhancement and Synthesis
· Multimodal and Cross-Domain Image Fusion
· Feature Representation and Embedding Methods
· Real-Time Image Processing and Edge Computing


Keywords

image processing, pattern recognition, feature extraction, object detection, image segmentation, real-time vision

Published Papers


  • Open Access

    ARTICLE

    Deep Incomplete Multi-View Clustering Based on Subspace Learning

    Jiao Wang, Tingting Song, Yunhui Zhou
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087751
    (This article belongs to the Special Issue: AI-Driven Image Processing and Pattern Recognition: Advances in Algorithms, Models, and Applications)
    Abstract In practical scenarios, multi-view data often contains missing entries caused by complicated data collection and transmission procedures, posing great challenges to clustering analysis. Existing incomplete multi-view clustering methods have two obvious limitations: (1) Imputation-based methods inevitably generate inaccurate information during data recovery; (2) Imputation-free methods struggle to balance cross-view consistency and complementarity. To tackle the above issues, this paper proposes a deep incomplete multi-view clustering approach based on subspace learning, which integrates latent feature extraction, K-nearest neighbor-based feature imputation, cross-view contrastive alignment, and attention-driven fusion within a unified deep learning framework. Specifically, we first leverage… More >

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