An official publication of the International Association for Pattern Recognition

Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. Examples include:

• Statistical, structural, syntactic pattern recognition;
• Neural networks, machine learning, data mining;
• Discrete geometry, algebraic, graph-based techniques for pattern recognition;
• Signal analysis, image coding and processing, shape and texture analysis;
• Computer vision, robotics, remote sensing;
• Document processing, text and graphics recognition, digital libraries;
• Speech recognition, music analysis, multimedia systems;
• Natural language analysis, information retrieval;
• Biometrics, biomedical pattern analysis and information systems;
• Special hardware architectures, software packages for pattern recognition.

We invite contributions as research reports or commentaries.

Research reports should be concise summaries of methodological inventions and findings, with strong potential of wide applications.
Alternatively, they can describe significant and novel applications of an established technique that are of high reference value to the same application area and other similar areas.

Commentaries can be lecture notes, subject reviews, reports on a conference, or debates on critical issues that are of wide interests.

To serve the interests of a diverse readership, the introduction should provide a concise summary of the background of the work in an accepted terminology in pattern recognition, state the unique contributions, and discuss broader impacts of the work outside the immediate subject area. All contributions are reviewed on the basis of scientific merits and breadth of potential interests.

Special Issue on Open the Brain: horizons and challenges in brain image analysis
Submission Date: 2025-05-20

The rapid advancement of Artificial Intelligence (AI) is profoundly impacting healthcare, particularly in brain-focused medical imaging. AI- based tools are revolutionizing diagnostics by improving speed, reducing costs, and enhancing accuracy.

Brain research through image analysis is a standout area, enabling preventive diagnoses, predicting disease progression, assisting in surgeries, and facilitating non-invasive therapy studies. These advancements are transforming the field and drawing interest from technical experts and industry stakeholders.

Despite significant progress, brain imaging faces challenges such as data scarcity, lack of standardization in image acquisition, labeling issues, and other technical hurdles. The brain's complexity and neurological diseases add further difficulties. AI is addressing these challenges by enhancing data analysis, improving imaging techniques, and offering robust predictive models, driving major advancements in brain research.

This special issue aims to explore critical aspects, innovative solutions, and prospects in brain-focused medical imaging. We aim to demonstrate how AI can overcome existing challenges and unlock new possibilities in diagnosing and treating neurological conditions. The issue will highlight cutting-edge research, discuss ethical and practical considerations of AI in healthcare, and provide a comprehensive overview of the current state and future directions of AI-driven brain imaging. Building on the recent ICPR 2024 challenge (https://iplab.dmi.unict.it/mfs/ms-les-seg/), this Special Issue invites numerous submissions in the related field, extending a special invitation to the top participants from the challenge.

Topics of interest

    Machine Learning and Deep Learning Algorithms for Brain Image Analysis
    Pattern Recognition for Early Diagnosis of Neurological Diseases
    Automated Brain Image Segmentation Techniques
    Enhancing Brain Image Quality and Artifact Detection
    Generative Models for Brain Image Synthesis and Augmentation
    Foundation Models in Brain Image Analysis
    Multimodal Data Integration for Brain Pattern Recognition
    Predictive Models for Neurological Disease Progression
    Ethical Considerations and Responsible Development of AI Systems for Brain Imaging

Guest editors:

Sebastiano Battiato, PhD
University of Catania, Catania, Italy
(sebastiano.battiato@unict.it)

Francesco Guarnera, PhD
University of Catania, Catania, Italy
(francesco.guarnera@unict.it)

Alessia Rondinella, PhD
University Campus Bio-Medico of Rome, Rome, Italy
(alessia.rondinella@unicampus.it)

Alessandro Ortis, PhD
University of Catania, Catania, Italy
(alessandro.ortis@unict.it)

Daniele Ravì, PhD
University College London, London, UK
(d.ravi@ucl.ac.uk)

Manuscript submission information:

The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from May 1st, 2025. When submitting your manuscript please select the article type VSI: HCBIA. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition Letters - ISSN 0167-8655 | ScienceDirect.com by Elsevier.

Important dates 

Submission Portal Open: May 1st, 2025
Submission Deadline: May 20th, 2025
Acceptance Deadline: September 30th, 2025

Keywords:

brain image analysis, medical imagina, medical generative data, neurological disease, multimodal data

Special Issue on Embodied Multi-Modal Data Fusion for Robot Continuous Perception
Submission Date: 2025-10-20

Embodied multi-modal data fusion represents a cutting-edge frontier in robotics, with the potential to revolutionize how robots perceive, understand, and interact with the world. By integrating diverse sensory modalities, it enables robots to operate autonomously and adaptively in dynamic, unstructured environments. As robots become increasingly integral to sectors such as healthcare, manufacturing, transportation, and services, the demand for robust, efficient, and intelligent perception systems is more critical than ever. Embodied multi-modal data fusion addresses these demands by leveraging state-of-the-art technologies—including sensor fusion, machine learning, and embodied cognition—to process complex sensory inputs, make real-time decisions, and adapt continuously to changing environments. This special issue on Embodied Multi-Modal Data Fusion for Robot Continuous Perception serves as a foundational resource, highlighting the field’s interdisciplinary nature and transformative potential. Covering topics such as multi-modal fusion algorithms, embodied cognition, and practical applications, it provides a comprehensive platform for researchers, engineers, and industry professionals to foster innovation and collaboration across disciplines.

Topics of interest：

We welcome submissions that present innovative theories, methodologies, and applications in embodied multi-modal data fusion for continuous robot perception.

Multi-Modal Data Fusion:

    Novel approaches for integrating diverse sensory modalities, including vision, radar, audio, tactile, and proprioception;
    Strategies for managing noisy, incomplete, or misaligned data in multi-modal fusion;
    Cross-modal learning and representation techniques to improve robot perception accuracy and robustness.

Embodied Perception:

    Robot perception systems that tightly integrate sensory inputs with robot kinematics, dynamics, and physical embodiment;
    Context-aware perception frameworks enabling adaptive and task-specific robot behaviors;
    Perception-action loops for real-time decision-making and interaction in dynamic environments.

Continuous Perception:

    Real-time processing of multi-modal sensory data streams to ensure continuous and uninterrupted robot perception;
    Temporal modeling techniques for dynamic environments, including spatiotemporal data fusion and sequential learning;
    Energy-efficient and resource-constrained algorithms for continuous robot perception on edge or embedded systems.

Learning and Adaptation:

    Self-supervised, unsupervised, and few-shot learning approaches for multi-modal robot perception
    Techniques for lifelong learning and adaptation in robots operating in evolving environments.
    Transfer learning and domain adaptation methods for cross-environment robot perception.

Guest editors:

Rui Fan, PhD
Tongji University, Shanghai, China
Email: rui.fan@ieee.org

Xuebo Zhang, PhD
Nankai University, Tianjin, China
Email: zhangxuebo@nankai.edu.cn

Hesheng Wang, PhD
Shanghai Jiao Tong University, Shanghai, China,
Email: wanghesheng@sjtu.edu.cn

George K. Giakos, PhD
Manhattan University, Riverdale, New York, United States
Email: george.giakos@manhattan.edu

Manuscript submission information:

The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from October 1st, 2025. When submitting your manuscript please select the article type VSI: EMDF-RCP. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition Letters - ISSN 0167-8655 | ScienceDirect.com by Elsevier.

Important dates 

Submission Portal Open: October 1st, 2025
Submission Deadline: October 20th, 2025
Acceptance Deadline: April 1st, 2026

Keywords:

Robot Learning; Robot Perception; Multi-Modal Perception; Continuous Learning; Embodied AI; Data Fusion

Special Issue on Pattern Recognition techniques to investigate Human Movements - handwriting and beyond
Submission Date: 2026-01-20

Handwriting analysis has been widely studied for many years contributing to the development of a research field, which produced many theoretical and experimental results. In this context, the automatic processing of writing and drawing features, both online and offline, has led to the development of interesting applications in different fields, among which the study of human movements has attracted great interest in recent years. The goal is to understand how handwriting can reflect and characterize human motor and cognitive processes, particularly concerning coordination, motor control, and mental representation of movements. The results of these studies have been used in different fields, such as Motor rehabilitation, to monitor and improve the reacquisition of motor skills after brain injuries or accidents, early neurological diagnosis, to identify neurological disorders such as Parkinson's or Alzheimer's through the analysis of altered writing patterns, and the monitoring of the development of writing skills in children to identify learning disabilities early. Although many results have been obtained in this field and several studies have been published in the literature, the analysis of human movements and the understanding of the complex mechanisms of fine motor control are still the subject of study and ongoing research, as well as the characterization of movement alterations due to the onset of neurodegenerative diseases.

This Special Issue aims to bring together the works of many experts in this multidisciplinary subject, which involves different competencies and knowledge. These span from the study of handwriting generation models and motor control mechanisms to the development of machine learning techniques for handwriting analysis and recognition. The Special Issue should allow us to highlight the advances on these topics from a wide-angle perspective and stimulate new theoretical and applied research to characterize the state of the art in this subject.

Topics of interest

    Movement analysis (signal/image/video processing, hand and finger movements, human pose estimation and tracking);
    Movement segmentation and gesture recognition (features extraction, classification, identification, and machine learning)
    Multimodal fusion and registration (with EEG, EMG, voice recording, eye tracking, multi-limb or contralateral movements)
    Handwriting, signature and movement synthesis, generative AI.
    Document analysis and recognition (print, handwriting, drawing, images)
    Applications in healthcare (health monitoring, detection of injuries, Parkinson's and Alzheimer's diseases)
    Application in education (handwriting learning and assessment, early screening of motor aptitude and disorders)
    Applications in arts (cultural heritage, painting, drawing)
    Applications in forensic and security (signature verification, writer identification, handwritten document forgery detection, biometric motion identification)

Guest editors:

Claudio De Stefano, PhD
University of Cassino and Southern Lazio, Cassino, Italy
Email: destefano@unicas.it

Réjean Plamondon, PhD
École Polytechnique, Montréal, Canada
Email: rejean.plamondon@polymtl.ca

Céline Rémi, PhD
University of the Antilles, Guadeloupe, France
Email: celine.remi@univ-antilles.fr

Tiziana D’Alessandro, PhD
University of Cassino and Southern Lazio, Cassino, Italy
Email: tiziana.dalessandro@unicas.it

Lama Séoud, PhD
Polytechnique Montréal, Montreal, Canada
Email: lama.seoud@polymtl.ca 

Manuscript submission information:

The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from January 1st, 2026. When submitting your manuscript please select the article type VSI: PRIHM. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition Letters - ISSN 0167-8655 | ScienceDirect.com by Elsevier.

Important dates 

Submission Portal Open: January 1st, 2026
Submission Deadline: January 20th, 2026
Acceptance Deadline: September 20th, 2026

Keywords:

Handwriting analysis and recognition, Movement analysis, Machine Learning, Information Fusion, Neurological Disorder Diagnosis