Journal Information
Pattern Recognition Letters (PRL)
http://www.journals.elsevier.com/pattern-recognition-letters/
Impact Factor:
3.255
Publisher:
Elsevier
ISSN:
0167-8655
Viewed:
23203
Tracked:
87
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 2021-03-20
Special Issues
Special Issue on Self-Learning Systems and Pattern Recognition and Exploitation (SeLSPRE)
Submission Date: 2021-10-20

Self-Learning Systems aim to achieve a goal -without being pre-programmed- in an environment that may be completely unknown initially. Self-learning algorithms are inspired by neuroscience and mimic he way the brain achieves cognition: they explore the environment following a try-and-error approach, or acquire knowledge from demonstrations provided by experts. The development of such a kind of systems is pushed forward by AI technologies such as Reinforcement Learning, Inverse Reinforcement Learning, and Learning by Demonstration. Their application spams from robotics and autonomous driving up to healthcare and precision medicine. This special issue focuses on pattern recognition and their successive exploitation by Self-Learning Systems. The way Inverse Reinforcement Learning or Learning by Demonstration extract patterns from ‘demonstrated trajectories’, and how such patterns are successively exploited by a self-learning algorithm to optimize its policy or fasten its learning process, is of interest of this special issue. Topics of interest Inverse Reinforcement Learning Learning-by-Demonstration and Imitation Learning Pattern Recognition via Inverse Reinforcement Learning Pattern Recognition from Demonstrations Pattern exploitation in Self-Learning Systems Pattern recognition in partially observable environments Action-State trajectories analysis for pattern recognition and reward engineering Pattern recognition and exploitation in Multi-Agent Self-Learning Systems Pattern recognition and exploitation in Hierarchical Self-Learning Systems
Last updated by Dou Sun in 2021-02-28
Special Issue on Computational Linguistics Processing in Indigenous Language (CLPIL)
Submission Date: 2021-11-20

Natural language processing (NLP) involves building models of the language environment and inferring the consequences of inter-language processing. In the Machine Learning (ML) research, this technology has traditionally been facilitated by a technique called state-of-the-art machine translation, in which a translation model is developed and using this the meaning of each word from the original language is extracted. This type of model can be extended to several different languages, and for this reason, it can be useful for words those are identical in meaning or form are found to have a common meaning in each language. Textual participation helps to facilitate natural language interpretation, to allow computer applications, and to characterize how the text is interpreted by natural language devices. Automated algorithms for lexical task participation may be extended to different applications in the processing of natural languages. In particular, automated parsing tools have a crucial role to play in developing a computational approach to natural language processing for general purposes. The aim of the virtual special issue is to investigate the computational complexity of indigenous languages and to provide a solution for a problem from an obvious point of view: How do we solve a classification problem? Natural language processing is the application of artificial intelligence to the English language. Additionally, this issue will give an introduction to the mathematical machinery behind the classification problem of indigenous language. The purpose of this issue is to summarize the research techniques related to the future trends in Artificial Intelligence (AI), computational engineering, information science, Natural Language Processing. This issue tends to present several interesting open problems with future research directions for data engineering, computational engineering, data science, Multilingual models, Social Media mining and big data. Topics of Interest Potential topics include, but are not limited to the following: Automated Language Translation and Grammar Correction Computational language Processing Distributional models and semantics Evolutionary language modeling for pattern recognition Indigenous language problems Lexical Knowledge Representation and pattern recognition Multilingual and cross-lingual distributional representations and universal language models Multimodal NLP, text-image and image-text processing Multimodal NLP: Audio, Image, Video Natural Language Toolkit for Virtual Libraries NLP for Remote Access E-Resources Opinion Mining and pattern recognition on social media Ontology based language pattern recognition Pattern Recognition using AI/ML Pattern recognition for virtual, augmented and mixed reality languages Privacy and security on language libraries Sentiments Pattern Analysis through NLP for Document Management Syntactic, semantic, and context parsing and analysis Speech synthesis and Pattern recognition
Last updated by Dou Sun in 2021-02-28
Special Issue on Deep Learning for Acoustic Sensor Array Processing (DL-ASAP)
Submission Date: 2022-03-20

Acoustic sensor array processing is a well-studied field that has provided solutions to a wide range of practical problems such as source detection, estimation of source number, localization and tracking, source separation and signal enhancement, acoustic recognition, noise reduction and dereverberation. Although traditional multichannel signal processing methods reached a high level of maturity from a theoretical prospective and have shown to perform fairly well in simple applications, acoustic sensing in complex real-world applications is still a challenging problem. Reverberation, complex noise fields, dynamic reconfiguration of the acoustic scene, interferences, and concurrent multiple sources, represent today some of the most challenging problems in acoustic sensor array processing. Recently, we have witnessed a growing interest in using artificial intelligence combined with sensor arrays to potentially solve acoustic sensing problems in complex environments and in emerging applications. Learning-based methods have shown to be able to exploit the multidimensional characteristics of a sensor array and marked the way to new solutions and novel applications. The proposed special issue aims to present recent advances in the development of artificial intelligence and deep learning methods for acoustic sensor array processing emphasizing the associated theory, models, and applications. Automatic computer audition and microphone arrays need novel methods that use modern deep learning array processing addressing the challenges raised by real-life applications. The Special Issue welcomes research papers covering innovative learning-based approaches, theoretical advances, technological improvements, and novel applications in the field.
Last updated by Dou Sun in 2021-05-22
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