Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal Pattern Recognition was established some 50 years ago, as the field emerged in the early years of computer science. Over the intervening years it has expanded considerably.

The journal accepts papers making original contributions to the theory, methodology and application of pattern recognition in any area, provided that the context of the work is both clearly explained and grounded in the pattern recognition literature. Papers whos primary concern falls outside the pattern recognition domain and which report routine applications of it using existing or well known methods, should be directed elsewhere. The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition. Submitted papers should be single column, double spaced, no less than 20 and no more than 35 (40 for a review) pages long, with numbered pages.

Special Issue on Graph Foundation Model for Medical Image Analysis
Submission Date: 2025-02-01

The integration of diverse medical imaging modalities, such as MRI, CT, PET, and histopathological images, presents significant opportunities for advancing precision medicine, diagnostics, and treatment strategies. However, the complexity of relationships within this imaging data poses unique challenges in data representation, fusion, and analysis. Graph foundation models, a powerful tool for capturing relationships and dependencies between imaging entities, are uniquely suited to address these challenges, especially in the context of medical imaging where connections between different types of images are crucial for holistic medical insights.This special issue is necessary to bring together the latest research contributions from both academia and industry, focusing specifically on the application of graph foundation models in medical image analysis. Unlike other general calls for machine learning or AI in healthcare, this special issue will provide a focused platform for exploring how graph foundation models can be harnessed to solve the unique problems associated with medical imaging data. The goal is to advance both the theoretical foundations and practical implementations of graph-based models in healthcare, bridging the gap between AI research and real-world medical applications.

    The special issue welcomes research contributions related to the following topics:Multimodal Integration Using Graph Foundation Models
    Graph Foundation Models for Segmentation, Detection, and Classification
    Explainable and Interpretable Graph Foundation Models
    Graph-Based Generative Models for Medical Image Synthesis
    Real-Time Graph Foundation Models in Clinical Practice
    Graph Foundation Models in Radiomics and Radiogenomics
    Clinical Applications of Graph Foundation Models for Personalized Medicine

Guest editors:

Yue Gao, PhD
Tsinghua University, Beijing, China
gaoyue@tsinghua.edu.cn 

Angelica I Aviles-Rivero, PhD
University of Cambridge, Cambridge, UK
ai323@cam.ac.uk 

Mingxia Liu, PhD
University of North Carolina at Chapel Hill, North Carolina, USA
mingxia_liu@med.unc.edu 

Manuscript submission information:

The journal submission system (Editorial Manager®) will be open for submissions to our Special Issue from October 15, 2024. When submitting your manuscript please select the article type VSI: Graph Foundation Model. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition - ISSN 0031-3203 | ScienceDirect.com by Elsevier.

Important dates 

Submission Portal Open: October 15, 2024
Submission Deadline: February 01, 2025
Acceptance Deadline: June 01, 2025

Keywords:

Foundation Model, Graph Neural Networks, Hypergraph Neural Networks, Medical Image Analysis

Special Issue on From bench to the wild: Recent Advances in Computer Vision methods (WILD-VISION)
Submission Date: 2025-03-31

The rapid advancement of visual pattern recognition systems has led to their transition from laboratory settings to real-world applications, where they face the challenges of distribution shifts and adversarial samples. This special issue focuses on innovative methodologies that enhance the robustness and generalization capabilities of visual classifiers on unknown data in diverse, uncontrolled environments, addressing key issues such as dataset imbalance, adversarial attacks, and the exploitation of multi-modal systems. Submissions are encouraged from researchers exploring neural network architectures, data augmentation, multi-task learning, and multi-sensor fusion techniques to improve performance in real-world conditions.

This special issue seeks to collect cutting-edge research that advances the generalization capabilities of visual classifiers under real-world conditions. The scope includes, but is not limited to, the development of robust neural network architectures, transformers, and machine learning models that address challenges such as distribution shift, adversarial attacks, and dataset imbalance. Contributions leveraging multi-task neural networks, multimodal approaches (e.g., vision-language models, multi-sensor fusion), and efficient, lightweight models for edge devices are highly encouraged. Papers should align with the broader topics of computer vision, image processing, multimedia systems, and biometrics, with a focus on improving real-world performance across various applications, including autonomous driving, cognitive robotics, and security-critical environments.

Topics of interest are but not limited to:

    Novel Neural Networks or other Architectures (e.g. Transformers) for Dealing with Distribution Shifts in the Wild
    Data Augmentation Strategies, Generative and Degradation models for Enhancing Generalization on Unseen Data
    Robustness against Adversarial Attacks
    Bias Mitigation in Unbalanced Datasets
    Multi-task vs Single-task Learning in Real-world Scenarios
    Resource-efficient Architectures for Edge Computing and (near) Real-time Processing
    Vision-Language Models and other Multi-modal Approaches
    Multi-sensor Fusion for Enhanced Performance
    New Datasets and Benchmarks for Computer Vision Systems in the Wild
    Novel Applications and Case Studies

Guest editors:

George Azzopardi, PhD
University of Groningen, Groningen, The Netherlands
E-mail: g.azzopardi@rug.nl 

Laura Fernández Robles, PhD
University of León, Leon, Spain
E-mail: l.fernandez@unileon.es 

Antonio Greco, PhD
University of Salerno, Fisciano, Italy
E-mail: agreco@unisa.it 

Bruno Vento, PhD
StudentUniversity of Naples Federico II, Napoli, Italy
E-mail: bruno.vento@unina.it 

Manuscript submission information:

The journal submission system (Editorial Manager®) will be open for submissions to our Special Issue from October 27, 2024. When submitting your manuscript please select the article type VSI: WILD-VISION. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition - ISSN 0031-3203 | ScienceDirect.com by Elsevier.

Important dates 

Submission Portal Open: October 27, 2024
Submission Deadline: March 31, 2025
Acceptance Deadline: September 01, 2025

Keywords:

Distribution Shift, Data Augmentation, Adversarial Robustness, Bias Mitigation, Multi-task Learning, Edge Computing, Vision-Language Models, Multi-modal Fusion, Real-world Benchmarks, Resource-efficient Architectures