The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23-27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

This work provides an instructive into applications and problems from the broad field of pattern recognition. It describes basic topics and the required mathematical background of image and speech processing. Algorithms and data structures for filtering, feature extraction, segmentation and classification are discussed, introducing and demonstrating different C++ concepts. The practice of object-oriented programming is illustrated by a step-wise development of a complete class library for image processing.

AI has contributed a lot and biometrics is no exception. To make AI solutions commercialized/fully functional, one requires trustworthy and explainable AI (XAI) solutions while respecting ethical issues. Within the scope of biometrics, the book aims at both revisiting ethical AI principles by taking into account state-of-the-art AI-guided tools and their responsibilities i.e., responsible AI. With this, the long-term goal is to connect with how we can enhance research communities that effectively integrate computational expertise (with both explainability and ethical issues). It helps combat complex and elusive global security challenges that address our national concern in understanding and disrupting the illicit economy.

This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field. Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition. This book is aimed at graduate students studying electrical engineering and/or computer science. Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra.

This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Also there will be a special track on computer-aided diagnosis on COVID-19 by CT and X-rays images. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human-computer interaction, databases, and performance evaluation.

With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields. Suitable for an introductory course on cluster analysis or data mining, with an in-depth mathematical treatment that includes discussions on different measures, primitives (points, lines, etc.) and optimization-based clustering methods, Cluster Analysis and Applications also includes coverage of deep learning based clustering methods. With clear explanations of ideas and precise definitions of concepts, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications may be used by students and researchers in various disciplines, working in data analysis or data science.

This book provides insights into smart ways of computer log data analysis, with the goal of spotting adversarial actions. It is organized into 3 major parts with a total of 8 chapters that include a detailed view on existing solutions, as well as novel techniques that go far beyond state of the art. The first part of this book motivates the entire topic and highlights major challenges, trends and design criteria for log data analysis approaches, and further surveys and compares the state of the art. The second part of this book introduces concepts that apply character-based, rather than token-based, approaches and thus work on a more fine-grained level. Furthermore, these solutions were designed for "online use", not only forensic analysis, but also process new log lines as they arrive in an efficient single pass manner. An advanced method for time series analysis aims at detecting changes in the overall behavior profile of an observed system and spotting trends and periodicities through log analysis. The third part of this book introduces the design of the AMiner, which is an advanced open source component for log data anomaly mining. The AMiner comes with several detectors to spot new events, new parameters, new correlations, new values and unknown value combinations and can run as stand-alone solution or as sensor with connection to a SIEM solution. More advanced detectors help to determines the characteristics of variable parts of log lines, specifically the properties of numerical and categorical fields. Detailed examples throughout this book allow the reader to better understand and apply the introduced techniques with open source software. Step-by-step instructions help to get familiar with the concepts and to better comprehend their inner mechanisms. A log test data set is available as free download and enables the reader to get the system up and running in no time. This book is designed for researchers working in the field of cyber security, and specifically system monitoring, anomaly detection and intrusion detection. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems. Forward-thinking practitioners, who would benefit from becoming familiar with the advanced anomaly detection methods, will also be interested in this book.

This book features high-quality research papers presented at the 4th International Conference on Computational Intelligence in Pattern Recognition (CIPR 2022), held at Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal, India, during 23 - 24 April 2022. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.

This volume constitutes selected papers presented during the Second International Conference on Intelligent Systems and Pattern Recognition, ISPR 2022, held in Hammamet, Tunisia, in March 2022. Due to the COVID-19 pandemic the conference was held online. The 22 full papers and 10 short papers presented were thoroughly reviewed and selected from the 91 submissions. The papers are organized in the following topical sections: computer vision; data mining; pattern recognition; machine and deep learning.

This two-volume set constitutes the proceedings of the Third International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022, which took place in Paris, France, in June 2022. The 98 full papers presented were carefully reviewed and selected from 192 submissions. The papers present new advances in the field of pattern recognition and artificial intelligence. They are organized in topical sections as follows: pattern recognition; computer vision; artificial intelligence; big data.

This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.

This book constitutes the refereed proceedings of the 16th International Conference on Augmented Cognition, AC 2022, held as part of the 23rd International Conference, HCI International 2022, which was held virtually in June/July 2022.The total of 1271 papers and 275 posters included in the HCII 2022 proceedings was carefully reviewed and selected from 5487 submissions. The AC 2022 proceedings aims to develop adaptive systems capable of extending the information management capacity of individuals through computing technologies and offers a broad range of theoretical and applied issues related to Augmented Cognition and its applications.

This book constitutes refereed proceedings of the 28th International Workshop on Frontiers of Computer Vision, IW-FCV 2022, held in Hiroshima, Japan, in February 2022. Due to the COVID-19 pandemic the conference was held online. The 24 full papers presented in this volume were thoroughly reviewed and selected from 63 submissions. The papers are organized according to the following topics: camera, 3D, and imaging; learning algorithm; object detection/segmentation; recognition/generation.

This two-volume set LNCS 13188 - 13189 constitutes the refereed proceedings of the 6th Asian Conference on Pattern Recognition, ACPR 2021, held in Jeju Island, South Korea, in November 2021. The 85 full papers presented were carefully reviewed and selected from 154 submissions. The papers are organized in topics on: classification, action and video and motion, object detection and anomaly, segmentation, grouping and shape, face and body and biometrics, adversarial learning and networks, computational photography, learning theory and optimization, applications, medical and robotics, computer vision and robot vision.

This two-volume set LNCS 13188 - 13189 constitutes the refereed proceedings of the 6th Asian Conference on Pattern Recognition, ACPR 2021, held in Jeju Island, South Korea, in November 2021. The 85 full papers presented were carefully reviewed and selected from 154 submissions. The papers are organized in topics on: classification, action and video and motion, object detection and anomaly, segmentation, grouping and shape, face and body and biometrics, adversarial learning and networks, computational photography, learning theory and optimization, applications, medical and robotics, computer vision and robot vision.

The 3-volume set LNAI 13280, LNAI 13281 and LNAI 13282 constitutes the proceedings of the 26th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2022, which was held during May 2022 in Chengdu, China. The 121 papers included in the proceedings were carefully reviewed and selected from a total of 558 submissions. They were organized in topical sections as follows: Part I: Data Science and Big Data Technologies, Part II: Foundations; and Part III: Applications.

String matching problems range from the relatively simple task of searching a single text for a string of characters to searching a database for approximate occurrences of a complex pattern. Recent years have witnessed a dramatic increase of interest in sophisticated string matching problems, especially in information retrieval and computational biology. This book presents a practical approach to string matching problems, focusing on the algorithms and implementations that perform best in practice. It covers searching for simple, multiple and extended strings, as well as regular expressions, and exact and approximate searching. It includes all the most significant new developments in complex pattern searching. The clear explanations, step-by-step examples, algorithm pseudocode, and implementation efficiency maps will enable researchers, professionals and students in bioinformatics, computer science, and software engineering to choose the most appropriate algorithms for their applications.

This book offers a systematic, comprehensive, and timely review on V-HAR, and it covers the related tasks, cutting-edge technologies, and applications of V-HAR, especially the deep learning-based approaches. The field of Human Activity Recognition (HAR) has become one of the trendiest research topics due to the availability of various sensors, live streaming of data and the advancement in computer vision, machine learning, etc. HAR can be extensively used in many scenarios, for example, medical diagnosis, video surveillance, public governance, also in human-machine interaction applications. In HAR, various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, abnormal activities, etc., are recognized. The data can be collected from wearable sensors or accelerometer or through video frames or images; among all the sensors, vision-based sensors are now the most widely used sensors due to their low-cost, high-quality, and unintrusive characteristics. Therefore, vision-based human activity recognition (V-HAR) is the most important and commonly used category among all HAR technologies. The addressed topics include hand gestures, head pose, body activity, eye gaze, attention modeling, etc. The latest advancements and the commonly used benchmark are given. Furthermore, this book also discusses the future directions and recommendations for the new researchers.

This book presents revised selected papers from the 18th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2021, held in Shanghai, China, in December 2021.The 41 papers presented in this volume were carefully reviewed and selected from 110 submissions. They were organized in topical sections on image analysis; quality assessment; target detection; video processing; big data.

The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.

This book constitutes the refereed proceedings of the 5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021, held in Istanbul, Turkey, in December 2021. Due to the COVID-19 pandemic, MedPRAI 2021 was held fully online. The 28 revised full papers and 4 short papers presented were thoroughly reviewed and selected from 72 submissions. The papers are covering the topics of recent advancements in different areas of pattern recognition and artificial intelligence, such as statistical, structural and syntactic pattern recognition, machine learning, data mining, neural networks, computer vision, multimedia systems, information retrieval, etc.

The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy,The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc.

Automating Linguistics offers an in-depth study of the history of the mathematisation and automation of the sciences of language. In the wake of the first mathematisation of the 1930s, two waves followed: machine translation in the 1950s and the development of computational linguistics and natural language processing in the 1960s. These waves were pivotal given the work of large computerised corpora in the 1990s and the unprecedented technological development of computers and software.Early machine translation was devised as a war technology originating in the sciences of war, amidst the amalgamate of mathematics, physics, logics, neurosciences, acoustics, and emerging sciences such as cybernetics and information theory. Machine translation was intended to provide mass translations for strategic purposes during the Cold War. Linguistics, in turn, did not belong to the sciences of war, and played a minor role in the pioneering projects of machine translation.Comparing the two trends, the present book reveals how the sciences of language gradually integrated the technologies of computing and software, resulting in the second-wave mathematisation of the study of language, which may be called mathematisation-automation. The integration took on various shapes contingent upon cultural and linguistic traditions (USA, ex-USSR, Great Britain and France). By contrast, working with large corpora in the 1990s, though enabled by unprecedented development of computing and software, was primarily a continuation of traditional approaches in the sciences of language sciences, such as the study of spoken and written texts, lexicography, and statistical studies of vocabulary.

This book constitutes the refereed proceedings of the 15th International Conference on Pattern Recognition and Information Processing, PRIP 2021, held in Minsk, Belarus, in September 2021. Due to the COVID-19 pandemic the conference was held online. The 17 revised full papers were carefully reviewed and selected from 90 submissions. The papers present a discussion on theoretical and applied aspects of computer vision, recognition of signals and images, the use of distributed resources, and high-performance systems.

This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.