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 2019-11-24
Special Issues
Special Issue on Biometric Presentation Attacks: handcrafted features versus deep learning approaches (BioPAth)
Submission Date: 2020-10-31

In the last decade, biometric technology has been rapidly adopted in a wide range of security applications. This approach to automatic verification of personal identity begins to play a fundamental role in personal, national and international security. Despite this, there are well-founded fears that the technology is vulnerable to spoofing, also known as a presentation attack. For example, fingerprint verification systems can be violated by using fingerprints made of a synthetic material, such as silicone, in which the ridges and valleys of the fingerprints of another individual who has access to the system are imprinted. Iris and face recognition systems can be violated using images or video sequences of the eyes or face of a registered user. Speech recognition systems can be violated through the use of repeated, synthesized or converted speech. In recent years there has been a considerable effort to develop spoof countermeasures or presentation attack detection (PAD) technology to protect biometric systems from fraud. A PAD method can improve the security level of biometric recognition systems. Most of the PAD methods proposed are based on the use of handcrafted features, designed by an in-depth knowledge of designers. An alternative approach based on deep learning approach is also possible. This special issue is expected to present original papers describing the very latest developments in spoofing and countermeasures. What are the approaches to the state of the art? What are the advantages and what are the limits of handcrafted features and deep learning approaches? Is an auto-adaptive approach possible? How much do these systems integrate with the corresponding match systems? The focus of the special issue includes, but is not limited to the following topics related to spoofing and countermeasures: Adversarial biometric recognition; Spoof detection based on deep learning; Spoof detection based on handcrafted features; Attack transferability in biometric applications; Design of robust forgery detectors; Vulnerability analysis of previously unconsidered spoofing methods; Advanced methods for standalone countermeasures; New evaluation protocols, datasets, and performance metrics for the assessment of spoofing and countermeasures;
Last updated by Dou Sun in 2020-01-04
Special Issue on Deep Learning for Precise and Efficient Object Detection
Submission Date: 2020-12-31

Object detection is one of the most challenging and important tasks of computer vision and is widely used in applications such as autonomous vehicle, biometrics, video surveillance, and human-machine interactions. In the past five years, significant success has been achieved with the development of deep learning, especially deep convolutional neural networks. Typical categories of advanced object detection methods are one-stage, two-stage, and anchor-free methods. Nevertheless, the performance in accuracy and efficiency is far from satisfying. On the one hand, the average precision of state-of-the-art object detection methods is very low (e.g., merely about 40% on the COCO dataset). The performance is even worse for small and occluded objects. On the another hand, to obtain precision the detection speed is very low. It is challenging to get a satisfying trade-off between the detection precision and speed. Therefore, much efforts have to be engaged to remarkably improve the performance of object detection in both precision and efficiency. This special issue will publish papers presenting state-of-the-art methods in dealing with the challenging problems of object detection within the framework of deep learning. We invite authors to submit manuscripts that are highly related to the topics of this special issue and which have not been published before. The topics of interest include, but are not limited to: Anchor and Anchor-free object detection Detecting small or occluded objects Context and attention mechanism for object detection Fast object detection algorithms New backbone for object detection Architecture search for object detection 3D object detection Object detection in challenging conditions Handling scale problems in object detection Improving localization accuracy Fusion of point cloud and images for object detection Relationship between object detection and other computer vision tasks. Large-scale datasets for object detection
Last updated by Dou Sun in 2019-11-24
Special Issue on Pattern Recognition-driven User Experiences (PRUE)
Submission Date: 2021-02-10

Games, search engines, e-commerce, infotainment, and many other services allow users a high degree of personalization; this evolution creates new needs, changes habits, and raises expectations. At the same time, the availability of new instruments is noticeably changing the kind of experience the users expect. The strong immersivity and high degree of realism of VR, MR, and AR are freeing the UX from the classic screen borders, with voice and gestures adding naturalness to the experience and keeping high the sense of users’ involvement and immersion. IoT ecosystems, smartwatches, digital assistants, and other devices, are instruments that may provide precious hints about users and usage contexts, if supported by the application of Pattern Recognition theories and techniques. Aiming at improving efficiency, intelligence, and delight perceived by users, Pattern Recognition-driven User Experience leverages intelligent computing to dynamically adapt appearance and behaviour with automatic decision-making. Pattern Recognition offers the instruments to detect and “understand” context, user’s signals, intents, emotions, and provides a set of disruptive methodologies for an effective personalization of the experience. The purpose of this Special Issue is to investigate how concepts and theories related to Pattern Recognition can be applied to improve or create a fully novel User Experience, new opportunities, and open problems. The Special Issue aims at collecting and presenting new advances in the application of Pattern Recognition to (but not limited): Emotion recognition and adaptive applications; Speech recognition; Pattern recognition for virtual, augmented and mixed reality; Applications to mobile and embedded systems; Natural language applications; Design and evaluation of innovative interactive system; Ambient intelligence; Personalization of user experience.
Last updated by Dou Sun in 2020-06-25
Submission Date: 2021-02-20

The integration of Machine Learning Intelligence and computer vision technologies has become a topic of increasing interest for both researchers and developers from academic fields and industries worldwide. Pattern recognition is defined as the classification of data based on the knowledge gained on statistical information extracted in the form of pattern. This special issue focus on pattern recognition and machine learning in solar. It is predictable that Machine Learning Intelligence will be the main approach of the next generation of computer vision research in Power and Energy System Applications. The explosive number of Machine Learning Intelligence algorithms and increasing computational power of computers has significantly extended the number of potential applications for computer vision and Energy Systems. It has also brought new challenges to the vision community. Authors are requested to submit unresolved and original unpublished research manuscripts focused on the latest findings in Machine Learning Intelligence and computer vision in Power and Energy. The topics include the following but are not limited to Content based Image retrieval in Solar Binocular-light field imaging system analytical hierarchical process multi-criteria decision-making system Machine learning adoption in Solar energy applications deep network architecture for multi-modal image super-resolution solar energy using layer-wise optimization algorithm Solution to Single image Super resolution Smart Vision-based Robotic Manipulation
Last updated by Dou Sun in 2020-06-07
Special Issue on Multi-view Representation Learning and Multi-modal Information Representation
Submission Date: 2021-03-31

During the recent decades, with the rapid development of information and computer technology, many fields have transformed data-poor areas to increasingly data-rich fields of research. Meanwhile, huge amount of data are often collected and extracted from multiple information sources and observed from various views. For example, a person can be identified by fingerprint, face, signature or iris with information obtained from multiple sources; an object can also be represented as multi-views, which can be seen as different feature subsets of the image; the news can be reported by a combination of texts, images, and videos on the internet; More and more information is represented by multi-view or multi modal data. To overcome the limitations of a single-view or single-modal data representation, different views and modals can be leveraged to provide complementary information to each other, and comprehensively characterize the data. Thus, multi-view representation learning and multi-modal information representation have raised widespread concerns in diverse applications. The main challenge is how to effectively explore the consistency and complementary properties from different views and modals for improving the multi-view learning performance. The goal of this special issue in Pattern Recognition Letters is to collect high-quality articles focusing on developments, trends, and research solutions of multi-view representation learning and multi-modal information representation in range of applications. The topics of interest include, but are not limited to: Ø Feature learning techniques (feature selection/reduction/fusion, subspace learning, sparse coding, etc.) for multi-view data. Ø Multi-view data based real-world applications, e.g., object detection/tracking, image segmentation, video understanding/categorization, scene understanding, action recognition, classification/clustering tasks, etc. Ø Advanced deep Learning techniques for multi-view data learning and understanding. Ø Structured/semi-structured multi-view data learning (e.g., one-shot learning, zero-shot learning, supervised learning, and semi-/unsupervised learning). Ø Multi-view missing data completion. Ø Multi-modal information retrieval and classification. Ø Large-scale multi-view data learning and understanding. Ø Multi-task/Transfer learning for multi-view data understanding. Ø Multi-modal data based medical applications (diagnosis, reconstruction, segmentation, registration, etc.) Ø Multi-modal data based medical image analysis with advanced deep learning techniques. Ø Multi-modal data based remote sensing image analysis. Ø Survey papers with regards to topics of multi-view representation learning and understanding. Ø New benchmark datasets collection for multi-view data learning.
Last updated by Dou Sun in 2020-03-27
Submission Date: 2021-04-01

COVID-19 disease, caused by the SARS- virus, was detected in December 2019 and declared a global pandemic on 11 March 2020 by the WHO. Artificial Intelligence (AI) is a highly effective method for fighting the pandemic COVID-19. AI can be described as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision applications for present purposes to teach computers to use large data-based models for pattern recognition, description, and prediction. Such functions can help identify (diagnosing), forecasting, and describing (treating) COVID-19 infections and aiding in controlling socioeconomic impacts. After the pandemic epidemic, for these reasons, there has been a rush to use and test AI and other data analytics devices. Such tasks can be useful for identifying (diagnosing), forecasting, and describing (treating) COVID-19 infections and helping to control socioeconomic impacts. Since the onset of the pandemic, there has been a rush to use and test AI and other data mining techniques for these purposes. The risk of the epidemic in terms of life and economic loss would be terrible; much confusion engulfed predictions of how bad and how effective non-pharmaceutical and pharmaceutical solutions would be. A worthy goal is to strengthen AI, one of the most popular data analytics tools that have been developed in the past decade or reduce these uncertainties. Data scientists have been willing to take up the opportunity. In AI, machine learning and its subset(Deep Learning) methods are employed in various applications to solve multiple problems that occur due to uncertainty. But these problems were solved with the help of data collected from the history of occurrences of the event. Most of the machine learning and deep learning algorithms are trained to address the supervised learning problem, where the algorithms know the prediction requirement. On the other hand, the potential measure of the unsupervised learning method is quite high. The ability to explore new possibilities of the outcome is high. In general, supervised learning methods are bounded with biases, in which the set of rules are determined with the DOs and DONTs, which prohibit the thinking of other possibilities. Also, a high effort, manual work, and time are required to label the data for the supervised learning process, in case the labeling is not available. The primary objective of this special issue is to enhance the ability of unsupervised learning into the deep learning methodologies to find a solution to the COVID-19. To improve the behavior and nature of the deep learning method with the quality of the clustering algorithm. So, the unsupervised learning methodology can be implemented in the deep learning algorithms for efficient data classification. The focus of this special issue is to provide a platform and opportunity for the researchers to find the solution for the current pandemic and future hazards like this that humanity has to face using the AI that involves self-learning methodologies. TOPICS MAY INCLUDE, BUT NOT LIMITED TO THE FOLLOWING: · intelligent signal computing based on Deep Embedded clustering · An evolutionary approach to Process the signals and its application · Architectures for Real-time sensing and intelligent processing · Auto-Encoders, Restricted Boltzmann Machines for signal classification · Real-time Signal processing based on DEC · Parallel and distributed algorithm design and implementation in signal sensing · Analytics for multi-dimension data · Intelligent computing on signal for data analysis · Real-time remote sensing signals, such as hyperspectral signal classification, content-based signal indexing, and retrieval, monitoring of natural. · the selection of suitable unsupervised learning methodologies. · the selection of suitable and efficient deep learning methodology. · the selection of diverse datasets and problems to test and validate the research outcomes. · the exploration of the optimal deep learning methodology for data classifications.
Last updated by Dou Sun in 2020-07-30
Submission Date: 2021-05-20

The rapid increase in population has predominantly increased the demand and usage of the motorized vehicles in all areas. This increase in motor vehicular usage has substantially increased the rate of road accidents in the recent decade. Furthermore, injuries, disabilities, and death due to fatal road accidents have been increasing every year despite the safety measures introduced for the public and private transportation system. Congestion of vehicles, a driver under alcohol or drug influence, distracted driving, street racing, faulty design of cars or traffic lights, tailgating, running red lights & stop signs, improper turns and driving in the wrong direction are some of the real causes of accidents across the globe. There are many advanced surveillance systems implemented for road safety, but the prevention of accidents are still being an effective problem. The existing sophisticated vehicles monitored and traffic surveillance system should be used to prevent accidents from occurring. However real-time observations are difficult with an enormous amount of surveillance data running continuously. With the emerging trends in the field of information and computer science, the use of innovative technologies in real-time can be helpful for accident prevention and detection. Computer vision is the technology that is designed to imitate how the human visual system works. The digital image data from the multiple surveillance systems are acquired in real-time and the data is analyzed and if there are any incidents such as speeding, reckless driving, accidents, etc. it is identified and reported by the system concurrently. Image classification, object detection, object tracking, semantic segmentation, and instance segmentation are some of the computer vision-based techniques with advanced deep learning approaches which can be used in the real-time accident detection and prevention processes. Similarly, using neural networks many anomalies can be detected in the movement of vehicles using historical data which can be also used in the prevention of accidents. The recent developments in the use of deep learning approaches in visual recognition can be seen as a significant contribution to advanced computer vision research. Moreover, the assistance of computer vision in the surveillance of traffic for accident prevention and detection in real-time would be more significant. The special issue on “Real-time computer vision for accident prevention and detection” The list of topics that are relevant includes, but it is not limited to, the following: Theoretical analysis of Computer Vision-based Visual recognition for Fatal Accidents Unsupervised, Semi-Supervised and Self-Supervised Feature Learning of Transportation Accidents A Study on Real-time Applications of Computer Vision and Image Analysis in Traffic Congestion Deep Vision-based Learning for Accident and Traffic Collision Reconstruction Future of Computer Vision in Road Safety and Intelligent Traffic Sensors and Early Vision for Post-Accident and Injury Phases Computer Vision for Fatigue Detection and Management Technologies Applications of Neural Networks in Transportation Strategy Planning and Instinctive Decision Making Advanced Visual Learning Methods for Risk-based Accident Prevention Computer Vision Algorithms and methodologies for Pre-Crash Analysis
Last updated by Dou Sun in 2020-07-30
Special Issue on Application of Pattern Recognition in Digital world: Security, Privacy and Reliability (APRDW)
Submission Date: 2021-06-20

Digital technology plays a vital role in humans’ day-to-day activity. It has made the system simple and more powerful and plays its major role in social networks, communication, and digital transaction, etc. The rapid development in digital technology also has downsides in the integrity of data, data privacy, and confidentiality. There has been a need for security, privacy, and reliability in digital technology. Pattern recognition is a computerized recognition that regulates the data in digital technology and plays a vital role in the digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms. The pattern recognition techniques have been categorized as statistical techniques, structural techniques, template matching, neural network approach, fuzzy model, and hybrid models. A common platform is always in need to share the views of different researchers relating to the complicated facets of pattern recognition in the areas of security, privacy, and reliability in digital technology. This special issue explores novel concepts and practices with a long-term goal of fully-automated lifestyle fostered by the technological advances of pattern recognition in a wide spectrum of applications. We invite authors from both industry and academia to submit original research and review articles that cover the security, privacy, and reliability in digital technology using the pattern recognition techniques. Models, algorithms, and designs for reliability in digital media Network-assisted rate adaptation for reliability in digital media Reliability based privacy in digital media Reliability, security in digital transaction Malware and virus detection for reliable digital media analytics Development of software tools and technique for integrity of data, data privacy, and confidentiality
Last updated by Dou Sun in 2020-08-11
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