Journal Information
Pattern Recognition Letters (PRL)
Impact Factor:
Call For Papers
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.
Last updated by Dou Sun in 2022-01-29
Special Issues
Special Issue on Recent Advances in Deep Learning Model Security
Submission Date: 2022-06-20

Deep learning has been widely used in a vast number of applications, such as natural language processing, computer vision, decision making, data security, etc. To learn effective deep learning models, it requires a lot of time and efforts for data collection and computation power allocation. These models may be illegally distributed for unauthorized usages, which sacrifices the interest of the model owners. On the other hand, the deep learning models are also vulnerable to adversarial samples or poisoned training data. This severely reduces the accuracy and reliability of deep learning-based techniques. The research of deep learning model security is to ensure the authenticity and reliability of deep learning models against various attacks. It can be mainly categorized into the area of 1) deep learning model attacks, such as the adversarial sample generation, data poisoning, training data reconstruction, and 2) deep learning model defense, such as model watermarking, adversarial sample detection, adversarial training. The research on deep learning model security is still in its infancy, though some progress have been made, it is far from satisfactory towards training a robust and reliable model for deep learning-based applications. This special issue aims at promoting the research on both the attacks and defense on deep learning models, which includes fast adversarial sample generation schemes, imperceptible data poisoning schemes, accurate training data reconstruction schemes, robust and traceable watermarking schemes for deep learning models, strategies for defending against the adversarial samples. Related researchers and engineers can contribute with original research that present their work. All submitted papers will be peer-reviewed and selected on the basis of both their quality and relevance to the theme of this special issue. Topics of interest for this special issue include, but are not limited to: Topics of interest Low complexity adversarial sample generation Adversarial sample generation for compressed multimedia data Adversarial sample generation for emerging data Imperceptible and effective data poisoning Accurate training data reconstruction Robust neural network watermarking Neural network fingerprinting Neural network authentication and tempering detection Effective strategies for adversarial training Effective models with adversarial training Accurate adversarial sample detection Generalizable and robust representation learning for face anti-spoofing Important Dates Manuscript submission deadline: June 20, 2022 Final acceptance deadline: January 31, 2023 Submission instructions Prospective authors are invited to upload their papers through the Editorial Manager system. The platform will start accepting submissions approximately one week before the submission period begins. When submitting your manuscript please select the article type “VSI:DLMS”. It is mandatory that the manuscripts be submitted by the deadline indicated. All manuscripts should adhere to the Journal’s guidelines: please take into account that Special Issue papers follow the same submission rules as regular articles. All submissions deemed suitable to be sent for peer review will be evaluated by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles. Please see an example here: Please make sure you have read the Guide for Authors before preparing your manuscript. The Guide for Authors and the link to submit your manuscript are available on the Journal’s homepage.
Last updated by Dou Sun in 2022-01-29
Special Issue on Advances and New challenges in Document Analysis, processing and Recognition at the Dematerialization Age
Submission Date: 2022-07-20

Description of the issue Document Analysis and Recognition (DAR) aims at the processing, extraction and recognition of information contained in documents and initially addressed to human comprehension. Almost all sectors (banks, public administrations, etc.) are living a digital transformation boosted by the current COVID pandemic emergency. Different technologies characterize this scenario: mobile devices, standard acquisition and processing tools, cloud computing, cybersecurity and privacy are just some examples. The document is now part of an integrated and extended system which not only considers it as a standalone element, but it is also linked to many different users and to other digital elements including other documents, metadata, digital contents, and database records. This special issue is devoted to present and collect the most recent advances to process documents in the stand-alone modality as well as in an integrated and extended way. Document indexing, Natural Language Processing, summarization and translation are crucial steps for digital document archiving and augmentation. Nevertheless, it is well known that digital document containing handwriting can convey very sensitive information about the writer: age, sex, emotion, identity and health status are just some examples. Under this light, the processing of these data is very relevant in many applications deserving specific investigation as well as privacy preserving. Papers presenting reviews, alternative perspectives, new applications and methods in the field of DAR are welcome. Research contributions should have a significant advancement both in the state-of-the-art for document analysis as well as for machine learning and pattern recognition fields. Topics of interest Topics of interest to this special issue include, but are not limited to: Document Layout Analysis evaluation, recognition, semantic information extraction and benchmarking Camera-based document scanning, processing, segmentation, and recognition Document Understanding and information extraction Structured and unstructured documents processing Natural language processing, information extraction and retrieval Handwritten/printed document images, information extraction and retrieval Document Indexing Handwritten text recognition Document typeface, script and writing analysis and recognition Object detection and structure modeling: tables, forms, identification documents, drawn sketches, etc. Biometrics: writer identification and signature verification Soft biometrics and meta-data extraction and manipulation Deep Learning vs Shallow Learning techniques in real and benchmarking scenarios Real-time document analysis Interoperability of systems (e.g. cross dataset evaluations, standards,etc.) Document augmentation Human-Document Interaction Large digital archives Deadlines Submission period: 1-20 July-2022 Guest Editors Dr. Donato Impedovo (ME) – University of Bari Aldo Moro (Italy) Dr. Byron Leite Dantas Bezerra - University of Pernambuco (Brazil) Dr. Alejandro H. Toselli - Northeastern University (USA) Dr. Giuseppe Pirlo - University of Bari Aldo Moro (Italy)
Last updated by Dou Sun in 2022-01-29
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