This book discusses human emotion recognition from face images using different modalities, highlighting key topics in facial expression recognition, such as the grid formation, distance signature, shape signature, texture signature, feature selection, classifier design, and the combination of signatures to improve emotion recognition. The book explains how six basic human emotions can be recognized in various face images of the same person, as well as those available from benchmark face image databases like CK+, JAFFE, MMI, and MUG. The authors present the concept of signatures for different characteristics such as distance and shape texture, and describe the use of associated stability indices as features, supplementing the feature set with statistical parameters such as range, skewedness, kurtosis, and entropy. In addition, they demonstrate that experiments with such feature choices offer impressive results, and that performance can be further improved by combining the signatures rather than using them individually. There is an increasing demand for emotion recognition in diverse fields, including psychotherapy, biomedicine, and security in government, public and private agencies. This book offers a valuable resource for researchers working in these areas.

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

This book constitutes the refereed proceedings of the 5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The 19 full papers presented were carefully reviewed and selected from 27 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement or segmentation.

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: * multilayer perceptron; * the Hopfield network; * associative memory models;* clustering models and algorithms; * t he radial basis function network; * recurrent neural networks; * nonnegative matrix factorization; * independent component analysis; *probabilistic and Bayesian networks; and * fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

This book constitutes the proceedings of the 9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 11 full and 2 short papers presented were carefully reviewed and selected from 42 submissions. They cover topics in AI Modeling, AI Analysis, AI and Mobile Applications, AI Architecture, AI Management, AI Engineering, Mobile backend as a service (MBaaS), User experience of AI and mobile services.

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 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 book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.

This book constitutes the proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assistend Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, but changed to a virtual format due to the COVID-19 pandemic. The 12 full papers included in this volume were carefully reviewed and selected from 18 submissions. They were organized in topical sections named: methods; learning; and applications.

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

This book highlights the field of selfie biometrics, providing a clear overview and presenting recent advances and challenges. It also discusses numerous selfie authentication techniques on mobile devices. Biometric authentication using mobile devices is becoming a convenient and important means of verifying identity for secured access and services such as telebanking and electronic transactions. In this context, face and ocular biometrics in the visible spectrum has gained increased attention from the research community. However, device mobility and operation in uncontrolled environments mean that facial and ocular images captured with mobile devices exhibit substantial degradation as a result of adverse lighting conditions, specular reflections and motion and defocus blur. In addition, low spatial resolution and the small sensor of front-facing mobile cameras further degrade the sample quality, reducing the recognition accuracy of face and ocular recognition technology when integrated into smartphones. Presenting the state of the art in mobile biometric research and technology, and offering an overview of the potential problems in real-time integration of biometrics in mobile devices, this book is a valuable resource for final-year undergraduate students, postgraduate students, engineers, researchers and academics in various fields of computer engineering.

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing.

Model-driven individual-based forest ecology and individual-based methods in forest management are of increasing importance in many parts of the world. For the first time this book integrates three main fields of forest ecology and management, i.e. tree/plant interactions, biometry of plant growth and human behaviour in forests. Individual-based forest ecology and management is an interdisciplinary research field with a focus on how the individual behaviour of plants contributes to the formation of spatial patterns that evolve through time. Key to this research is a strict bottom-up approach where the shaping and characteristics of plant communities are mostly the result of interactions between plants and between plants and humans. This book unites important methods of individual-based forest ecology and management from point process statistics, individual-based modelling, plant growth science and behavioural statistics. For ease of access, better understanding and transparency the methods are accompanied by R code and worked examples.

Sicherheitsmanagement wird mit Hilfe des vorliegenden Handbuchs und der Software Ariadne SMS effizient und kostengunstig. Die Autoren beschreiben die Digitalisierung des Expertenwissens von Unfalluntersuchern und dessen maschinelle Verarbeitung. Das gesamte Unfallgeschehen wird mit algorithmisch auswertbaren Schlusseln strukturiert klassifiziert. Unfalldeskriptoren und Fehlervariablen werden auf der Basis des 3-Ebenen-Modells der Unfallentstehung zu Risikoprofilen und Praventionsmassnahmen verarbeitet. Ariadne SMS basiert auf aktueller Web-IT und ist durch selbstlernende Netze innovativ. Automatisierte Muster- und Spracherkennungsverfahren generieren valide Risikovorhersagen und Simulationen der Wirksamkeit von Massnahmen auf die Risikoverteilung. Fehlerquellen entfallen, Bearbeitungsschritte werden eingespart, Informationen sind transparent und jederzeit verfugbar. Der praktische Einsatz bei Bundeswehr und Berufsgenossenschaften fuhrte zu erheblichen Einsparungen. Anwendungen in Medizin und Unternehmensfuhrung, im Umwelt- und Katastrophenschutz sowie bei Versicherungen sind moeglich.

This book gathers high-quality research papers presented at the International Conference on Computing in Engineering and Technology (ICCET 2020) [formerly ICCASP]. A flagship conference on engineering and emerging next-generation technologies, it was jointly organized by Dr. Babasaheb Ambedkar Technological University and MGMs College of Engineering, Nanded, India on 9-11 January 2020. Focusing on applied computer vision and image processing, this proceedings volume includes papers on image processing, computer vision, pattern recognition, and DSP/DIP applications in healthcare systems.

For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.

The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings, and solutions, and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods.This, the 37th issue of the Transactions on Computational Science, is devoted to the area of Computer Graphics. The 9 papers included in the volume constitute extended versions of selected papers presented at the 36th Computer Graphics International Conference, CGI 2019. Topics covered include virtual reality, augmented reality, image retrieval, animation of elastoplastic material, and visualization of 360 DegreesHDR images.

This book provides a comprehensive guide to the emerging field of network slicing and its importance to bringing novel 5G applications into fruition. The authors discuss the current trends, novel enabling technologies, and current challenges imposed on the cellular networks. Resource management aspects of network slicing are also discussed by summarizing and comparing traditional game theoretic and optimization based solutions. Finally, the book presents some use cases of network slicing and applications for vertical industries. Topics include 5G deliverables, Radio Access Network (RAN) resources, and Core Network (CN) resources. Discusses the 5G network requirements and the challenges therein and how network slicing offers a solution Features the enabling technologies of future networks and how network slicing will play a role Presents the role of machine learning and data analytics for future cellular networks along with summarizing the machine learning approaches for 5G and beyond networks

This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.

This book contains the revised selected papers of 4 workshops held in conjunction with the International Conference on High Performance Computing, Networking, Storage and Analysis (SC) in November 2017 in Denver, CO, USA, and in November 2018 in Dallas, TX, USA: the 6th and 7th International Workshop on Extreme-Scale Programming Tools, ESPT 2017 and ESPT 2018, and the 4th and 5th International Workshop on Visual Performance Analysis, VPA 2017 and VPA 2018. The 11 full papers of ESPT 2017 and ESPT 2018 and the 6 full papers of VPA 2017 and VPA 2018 were carefully reviewed and selected for inclusion in this book. The papers discuss the requirements for exascale-enabled tools as well as new approaches of applying visualization and visual analytic techniques to large-scale applications. Topics of interest include: programming tools; methodologies for performance engineering; tool technologies for extreme-scale challenges (e.g., scalability, resilience, power); tool support for accelerated architectures and large-scale multi-cores; tool infrastructures and environments; evolving/future application requirements for programming tools and technologies; application developer experiences with programming and performance tools; scalable displays of performance data; case studies demonstrating the use of performance visualization in practice; data models to enable scalable visualization; graph representation of unstructured performance data; presentation of high-dimensional data; visual correlations between multiple data sources; human-computer interfaces for exploring performance data; and multi-scale representations of performance data for visual exploration.

Smart Homes (SH) offer a promising approach to assisted living for the ageing population. Yet the main obstacle to the rapid development and deployment of Smart Home (SH) solutions essentially arises from the nature of the SH field, which is multidisciplinary and involves diverse applications and various stakeholders. Accordingly, an alternative to a one-size-fits-all approach is needed in order to advance the state of the art towards an open SH infrastructure. This book makes a valuable and critical contribution to smart assisted living research through the development of new effective, integrated, and interoperable SH solutions. It focuses on four underlying aspects: (1) Sensing and Monitoring Technologies; (2) Context Interference and Behaviour Analysis; (3) Personalisation and Adaptive Interaction, and (4) Open Smart Home and Service Infrastructures, demonstrating how fundamental theories, models and algorithms can be exploited to solve real-world problems. This comprehensive and timely book offers a unique and essential reference guide for policymakers, funding bodies, researchers, technology developers and managers, end users, carers, clinicians, healthcare service providers, educators and students, helping them adopt and implement smart assisted living systems.

This book constitutes the refereed proceedings of the 9th International Workshop on Biomedical Image Registration, WBIR 2020, which was supposed to be held in Portoroz, Slovenia, in June 2020. The conference was postponed until December 2020 due to the COVID-19 pandemic. The 16 full and poster papers included in this volume were carefully reviewed and selected from 22 submitted papers. The papers are organized in the following topical sections: Registration initialization and acceleration, interventional registration, landmark based registration, multi-channel registration, and sliding motion.

This book presents a comprehensive review of heterogeneous face analysis and synthesis, ranging from the theoretical and technical foundations to various hot and emerging applications, such as cosmetic transfer, cross-spectral hallucination and face rotation. Deep generative models have been at the forefront of research on artificial intelligence in recent years and have enhanced many heterogeneous face analysis tasks. Not only has there been a constantly growing flow of related research papers, but there have also been substantial advances in real-world applications. Bringing these together, this book describes both the fundamentals and applications of heterogeneous face analysis and synthesis. Moreover, it discusses the strengths and weaknesses of related methods and outlines future trends. Offering a rich blend of theory and practice, the book represents a valuable resource for students, researchers and practitioners who need to construct face analysis systems with deep generative networks.

This book is an investigation into the problems of generating natural language utterances to satisfy specific goals the speaker has in mind. It is thus an ambitious and significant contribution to research on language generation in artificial intelligence, which has previously concentrated in the main on the problem of translation from an internal semantic representation into the target language. Dr. Appelt's approach, based on a possible-worlds semantics of an intensional logic of knowledge and action, enables him to develop a formal representation of the effects of illocutionary acts and the speaker's beliefs about the hearer's knowledge of the world. The theory is embodied and illustrated in a computer system, KAMP (Knowledge and Modalities Planner), described in the book.

This book constitutes refereed proceedings of the 26th International Workshop Frontiers of Computer Vision, IW-FCV 2020, held in Ibusuki, Kagoshima, Japan, in February 2020. The 27 full papers presented were thoroughly reviewed and selected from 68 submissions. The papers in the volume are organized according to the following topics: real-world applications; face, pose, and action recognition; object detection and tracking; inspection and diagnosis; camera, 3D and imaging.