Journal Information
Pattern Recognition Letters (PRL)
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Call For Papers
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.
Last updated by Dou Sun in 2024-07-14
Special Issues
Special Issue on Synthetic Images to Support Computer-Aided Diagnosis Systems
Submission Date: 2024-07-31

Today's health systems collect and deliver most medical data in digital format, mainly thanks to the scientific and technological advances that have led to digitization and increased generation and collection of data describing real-world applications or processes. The availability of medical data enables a large number of artificial intelligence applications, and there is growing interest in quantitative analysis of clinical images, such as Positron Emission Tomography, Computerized Tomography, and Magnetic Resonance Imaging. In addition, machine and deep learning models and data-driven artificial intelligence applications have proven to improve the management and decision-making to improve the discovery of new therapeutic tools, support diagnostic decisions, aid in the rehabilitation process, etc. Despite the potential of data-driven solutions, many problems prevent or delay the development of such solutions. For example, the increasing amount of available data can lead to increased effort to make a diagnosis and is even more challenging due to high inter/intra patient variability, the availability of different imaging techniques, the absence of completely standard acquisition procedures, and the need to consider data from multiple sensors and sources. Additional relevant issues are data access and the representativeness of the captured sample compared to the actual population. Access to real data may be delayed or even prevented for various reasons, such as privacy, security, and intellectual property, or the development of the necessary (quality) acquisition and preparation technology. Sample representativeness is another critical issue involving class imbalance and the representation of rare and extreme events, which is crucial for the performance of artificial intelligence models. For these reasons, researchers have recently explored the use of synthetic data (SD) with three different use cases regarding (i) data augmentation to balance data sets or supplement available data before training a model, (ii) privacy preservation to enable secure and private sharing of sensitive data; and (iii) simulation: to estimate and teach systems in situations that have not been observed in actual reality. The main goals of this special issue are to bring together diverse, new, and impactful research on synthetic data generation for biomedical imaging with a powerful impact on Computer-Aided Diagnosis systems for real-world clinical applications. Guest editors: Andrea Loddo, PhD University of Cagliari, Cagliari, Italy Lorenzo Putzu, PhD University of Cagliari, Cagliari, Italy Cecilia Di Ruberto, PhD University of Cagliari, Cagliari, Italy Carsten Marr, PhD Helmholtz Center Munich German Research Center for Environmental Health, Neuherberg, Germany Albert Comelli, PhD Ri.MED Foundation, Palermo, Italy Alessandro Stefano, PhD National Research Council, Roma, Italy Manuscript submission information: The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from July 1st, 2024. When submitting your manuscript please select the article type VSI: SISCAD. 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 | by Elsevier. Important dates Submission Portal Open: July 1st, 2024 Submission Deadline: July 31st, 2024 Acceptance Deadline: December 9th, 2024 Keywords: Computer-Aided Diagnosis; Privacy-Preserving; Synthetic Images; Medical Image Translation; Medical Image Generation; Domain Adaptation; Domain Generalisation
Last updated by Dou Sun in 2024-07-14
Special Issue on Pattern recognition in multimodal information analysis: observation, extraction, classification, and interpretation
Submission Date: 2024-09-20

In the information age, we grapple with a flood of diverse data types like text, images, audio, and video. AI's strides in single-modal analysis are notable, but the challenge lies in efficiently handling massive multimodal data to enhance machines' understanding of the world through pattern recognition. Advancements, in this area have led to techniques. For example, the use of image matching in scenarios involving modes is crucial in diagnostics, remote sensing, and computer vision. Coordinating the retrieval of data from modes improves the accuracy of pattern recognition while integrating audio video data enhances speech recognition and strengthens accident monitoring capabilities. In other words, multimodal learning and representation yield convincingly better results with confidence. However, there are still challenges that need to be addressed, such as handling types of data transforming data effectively enhancing datasets and ensuring interpretability of models, for processing data. In this context, this special issue outlines recent advances in the pattern recognition field, intending to bring together the work of scholars in this multidisciplinary subject, drawing on the different skills and knowledge of pattern recognition approaches applied in the multimodal information analyzing from the perspective of observing, extraction, classifying and interpretation. Guest editors: Jingsha He, PhDBeijing University of Technology, Beijing, Danilo Avola, PhDSapienza University of Rome, Roma, KC Santosh, PhDUniversity of South Dakota, Vermillion, Mario Molinara, PhDUniversity of Cassino and Southern Lazio, Cassino, Daniele Salvati, PhDUniversity of Udine, Udine, Manuscript submission information: The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from September 1st, 2024. When submitting your manuscript please select the article type VSI:PRMIA. 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 | by Elsevier. The submissions should be original and technically sound, and they should not have been published previously, nor be under consideration for publication elsewhere. If the submissions are extended works of previously published papers, the original works should be quoted in the References and a description of the changes that have been made should be provided. Important dates Submission Portal Open: September 1st, 2024 Submission Deadline: September 20th, 2024 Acceptance Deadline: December 15th, 2024
Last updated by Dou Sun in 2024-02-01
Special Issue on Trusty Visual Intelligence for Industry
Submission Date: 2024-10-20

Visual intelligence (VI) has revolutionized industries with their remarkable capabilities in image understanding and analysis. In recent years, there are many successful applications of VI technologies in industries, for example, using deep learning to train computers to monitor product quality. However, a salient fact is that the trustiness of visual technologies directly affects industrial production efficiency, product quality, safety, and traceability. Trusty VI may make the industrial operations much more efficient, improve resource (including human and material resources) utility and energy efficiency, and even help economic, environmental, and social sustainability.The motivation of this special issue is to advance trusty visual intelligence of industries, which connects to the industrial processes directly. We invite contributions that explore innovative methodologies and effective applications of visual analytics methods in industries. Topics of interest: Trusty imbalanced learning for industry Interpretable deep learning models for industry Knowledge embedded methods for industry Trusty visual intelligence technologies for process monitoring Trusty visual intelligence technologies for manufacturing Trusty visual intelligence technologies for quality inspection Trusty visual intelligence technologies for preventive maintenance Trusty visual intelligence technologies for robotics Automatic Annotation Tools for Image Data Other trusty visual intelligence techniques and applications
Last updated by Dou Sun in 2024-04-01
Special Issue on Deep Learning Models for Computer Vision in Medical Diagnosis
Submission Date: 2024-11-20

Convolutional Neural Networks (CNNs) serve as the cornerstone of contemporary deep learning methods for computer vision, fundamentally transforming the analysis of visual data. This revolution stems from the incorporation of convolutional layers, pooling layers, and fully connected layers, working collaboratively to progressively develop a nuanced understanding of input images. In the realm of medical applications, computer vision algorithms play a pivotal role in diagnosing imaging disorders, leveraging deep learning architectures to learn from both non-image and picture data through conventional deep networks and convolutional networks, respectively. The integration of deep learning in medical imaging and analysis empowers physicians and surgeons to gain clearer insights into a patient's body, facilitating the identification of potential issues or anomalies. This application spans various medical imaging modalities, including endoscopy, MRI, ultrasound, X-ray radiography, and more. Object detection algorithms, a key component of medical picture analysis, are frequently employed to identify initial abnormality symptoms in patients. Noteworthy examples include the identification of lung nodules on chest CT or X-ray images and the detection of breast lesions on mammography and ultrasound pictures. In radiology, deep learning algorithms are applied to identify anomalies or diseases from X-ray images, categorizing them into different illness types or severity levels. This work often leverages various machine learning algorithms that have been optimized either theoretically or empirically. Within the domain of medical imaging, deep learning algorithms exhibit unparalleled precision, effectively segmenting organs or structures, classifying images, identifying anomalies, and even forecasting the course of diseases. Deep learning has emerged as a critical technique for ultrasonic image recognition, significantly enhancing diagnostic accuracy and providing valuable guidance to medical professionals assessing a patient's condition. Moreover, deep learning contributes significantly to drug discovery by aiding in the development and discovery of medicines. Patient medical histories are meticulously scrutinized, and treatment plans are formulated based on the findings. These applications extend across various industries, including medical devices and automated driving. Initial research has primarily focused on identifying conditions such as glaucoma, age-related macular degeneration, and referable diabetic retinopathy. In the automated image analysis of fundus photos and optical coherence tomography pictures, deep learning has demonstrated promising outcomes. Computer vision, empowered by deep learning, excels in diagnosing medical images with superior precision, speed, and accuracy, making fewer mistakes by identifying intricate patterns in the images. Computer vision algorithms extract imperceptible information from medical photographs, contributing to tasks involving prediction or decision-making. Currently, convolutional neural network models, limited Boltzmann machine models, and sparse models are the most frequently utilized deep learning models in computer vision. Although these models share similarities in image recognition and classification, nuances exist in feature extraction. In light of these advancements, we invite submissions and articles for a thematic article collection dedicated to Deep Learning Models for Computer Vision in Medical Diagnosis. Potential topics include but are not limited to the following: Explainable AI Techniques in Deep Learning Models for Interpretable Medical Image Diagnosis Transfer Learning Approaches for Enhancing Generalization in Medical Computer Vision Models Imbalanced Datasets on the Performance of Deep Learning Models in Medical Imaging Multi-Modal Data for Comprehensive Medical Diagnosis Using Deep Learning Architectures Robustness and Security of Deep Learning Models in Medical Image Classification Tasks Novel Hybrid Architectures by Integrating Classical Image Processing Techniques with Deep Learning Models Adversarial Attacks on the Reliability of Deep Learning Models for Medical Image Analysis Generalization Capabilities of Pre-trained Models for Cross-Domain Medical Image Diagnosis Scalability and Efficiency of Deep Learning Models for Real-time Medical Diagnosis Applications Domain Adaptation Techniques to Enhance Robustness of Computer Vision-Based Medical Models: A future Perspective Future of Clinical Metadata for Holistic Patient Diagnosis using Deep Learning Approaches in Medical Imaging Guest editors: Dr. Roseline Oluwaseun Ogundokun, PhDLandmark University Omu Aran, Kwara State,; Dr. Akinbowale Nathaniel Babatunde, PhDKwara State University, Malete, Kwara State, Dr. Micheal Olaolu Arowolo, PhDBond Life Sciences Centre University of Missouri, Columbia, Manuscript submission information: The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from November 1st, 2024. When submitting your manuscript please select the article type VSI: DLMCVMD. 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 | by Elsevier. Important dates Submission Portal Open: November 1st, 2024 Submission Deadline: November 20th, 2024 Acceptance Deadline: March 25th, 2025
Last updated by Dou Sun in 2024-05-12
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