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;
• Scientific, engineering, social and economical applications of pattern recognition;
• 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 Pattern recognition in multimodal information analysis: observation, extraction, classification, and interpretation
截稿日期: 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, Chinajhe@bjut.edu.cn
Danilo Avola, PhDSapienza University of Rome, Roma, Italyavola@di.uniroma1.it
KC Santosh, PhDUniversity of South Dakota, Vermillion, USAsantosh.kc@usd.edu
Mario Molinara, PhDUniversity of Cassino and Southern Lazio, Cassino, Italym.molinara@unicas.it
Daniele Salvati, PhDUniversity of Udine, Udine, Italydaniele.salvati@uniud.it

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 | ScienceDirect.com 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 

Special Issue on Trusty Visual Intelligence for Industry
截稿日期: 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