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Advances In Pattern Analysis And Intelligent Sensing

Zhang C: Advanced Randomized Neural
Networks For Pattern Analysis

Advanced Randomized Neural Networks For Pattern Analysis

In brief, this book provides the advanced sensing technologies including multi-sensor collaborative sensing for pulse signal acquisition, sensor array design for breath odor perception, and voice signal acquisition. Besides, this book summarizes the advances about pulse feature extraction with improved Gabor function, odor feature extraction based on de-convolution methods. This book also presents the latest recognition methods including evolutionary ensemble model to handle the imbalance problem, multi-feature complementary methods for face image, tongue images, and pulse analysis. Multi-modal hybrid fusion and self-adaptive score fusion for disease classification are proposed and proved effective for medical biometrics.Medical biometrics achieves disease detection and health monitor by sensing and recognizing multi-modal body surface information. It is believed that body surface information, e.g., face, tongue, breath odor, voice, and wrist pulse, is one of the most important biometric features that contains rich pathological information and can be used for health assessments or disease detection. Focusing on the latest progresses in medical biometrics, this book systematically presents advanced signal acquisition techniques, feature extraction methods, and recognition algorithms.Both researchers and engineers in pattern recognition and medical diagnosis will benefit from this book as it offers comprehensive understanding of the advanced signal acquisition techniques, related feature extraction methods, and state-of-the-art analysis methods.

Advanced Medical Biometrics

Tiny Machine Learning Techniques for Constrained Devices

This textbook provides students and users with the knowledge necessary to successfully acquire and process image data. The fact that image acquisition and image processing are based on similar mathematical concepts is recognized throughout. Great emphasis has been placed on presenting the sometimes complex relationships in a descriptive and mathematically sound manner.
&nbsp;
This 7th edition of the successful textbook, first published in 1991, has been restructured. The section on image acquisition has been significantly expanded to reflect the rapid developments in image sensor technology, optics, and methods for reconstructing three-dimensional worlds from image data. The emphasis is on explaining the basic concepts so that readers can understand and learn to optimize the variety of image acquisition methods that may seem confusing at first.
&nbsp;
Artificial intelligence methods are not included in this book. Instead, this book provides the necessary knowledge about image acquisition and processing to apply machine learning methods to image analysis effectively.
&nbsp;
The Author
Bernd Jähne is senior professor at the Interdisciplinary Center for Scientific Computing (IWR) and the Institute of Environmental Physics (IUP) of Heidelberg University. From 2008 to 2017 he was coordinating director of the Heidelberg Collaboratory for Image processing (HCI), an industry-on-campus project of Heidelberg University and from 2008 to 2016 deputy director of the IWR. He is also founder and chair of the Heidelberg Image Processing Forum since 1995, chair of the EMVA 1288 standardization group since 2008, and vice president of the European Machine Vision Association from 2021 -2024. He is author of a several text books and handbooks on Digital Image Processing and Computer Vision published in German, English, and Russian language.

This textbook provides students and users with the necessary knowledge to successfullyto successfully acquire and process image data. It is consistently taken into account that image acquisition and image processing are based on the same mathematical concepts. Great emphasis was placed to present the sometimes complex relationships both clearly and mathematically sound.The material in the 7th edition of this textbook, which has been successful since 1991, has been restructured.In line with the rapid development of image sensor technology, optics and image sensor technology, optics and the diverse methods of reconstructing the three-dimensional world from image data. The focus was placed on working out the basic concepts. This enables the reader tounderstand the initially confusing variety of image acquisition methods and learn how to use them optimally. Artificial intelligence methods have deliberately not been integrated into the book. Rather, this book contains the necessary knowledge about image acquisition and processing in order to apply machine learning methods to image analysis in the best possible way.

Digital Image Processing and Image Formation

Computational Intelligence in Surveillance Systems Using Image Processing

An authoritative deep dive into the most recent machine learning approaches to hyperspectral remote sensing image processing In Machine Learning-Based Hyperspectral Image Processing, distinguished researcher Dr. Bing Zhang delivers an up-to-date discussion of machine learning-based approaches to hyperspectral image analysis. The author comprehensively reviews machine learning approaches to hyperspectral image denoising and super-resolution tasks, offering coverage of a variety of perspectives. Dr. Zhang also explores the most recent research on machine learning hyperspectral unmixing methods and hyperspectral image classification. He explains the algorithms used for hyperspectral image target and change detection, as well. Readers will also find: <ul> <li>A thorough introduction to the novel concept of applying advanced machine learning techniques to the analysis of hyperspectral imagery</li> <li>Comprehensive explorations of the most recent developments in this technology and its applications</li> <li>Practical discussions of how to effectively process and extract valuable insights from hyperspectral data</li> <li>Complete treatments of a variety of hyperspectral remote sensing image processing tasks, including classification, target detection, and change detection.</li> </ul> Perfect for postgraduate students and research scientists with an interest in the subject, Machine Learning-Based Hyperspectral Image Processing will also benefit researchers, academicians, and students engaged in machine learning-based approaches to image analysis.

Machine Learning-Based Hyperspectral Image Processing

Autonomous Cyber Resilience

Methods, Applications, and Implications

Adaptive AI in Sensor Informatics

Progress In Data Science

Ubiquitous Laplacian: An Introduction To Numerical Pdes With Applications In Data Science

A Path to Sustainable Development Goals

AI and ML Techniques in IoT-based Communication

Building a Program for Strategy and Oversight

Practical AI Governance

Manage the complexity of AI in any organization with this actionable guide to operationalizing AI governance, navigating regulatory challenges and future-proofing systems.

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Deep Learning Crash Course

Advances in Computer Vision and Pattern Recognition

This comprehensive and innovative handbook covers several aspects of biometrics from the perspective of recognizing individuals at a distance, in motion, and under a surveillance scenario. The book presents both a broad coverage and an in-depth investigation into the most advanced biometric technologies for recognition at a distance, with applications in areas such as border control, surveillance in critical infrastructures, and ambient intelligence.

This comprehensive and innovative handbook covers several aspects of biometrics from the perspective of recognizing individuals at a distance, in motion, and under a surveillance scenario.

For Surveillance and Security

Handbook of Remote Biometrics

Advanced Randomized Neural Networks For Pattern Analysis

Weitere Titel der Reihe: Advances In Pattern Analysis And Intelligent Sensing

Weitere Titel zum Thema