Books in Computer vision and pattern recognition

Computational Knowledge Vision

1st Edition
August 19, 2024
Wenbo Zheng + 1 more
English
Paperback
9 7 8 0 4 4 3 2 1 6 1 9 0
eBook
9 7 8 0 4 4 3 2 1 6 1 8 3

Computational Knowledge Vision: The First Footprints presents a novel, advanced framework which combines structuralized knowledge and visual models. In advanced image and visual perception studies, a visual model's understanding and reasoning ability often determines whether it works well in complex scenarios. This book presents state-of-the-art mainstream vision models for visual perception. As computer vision is one of the key gateways to artificial intelligence and a significant component of modern intelligent systems, this book delves into computer vision systems that are highly specialized and very limited in their ability to do visual reasoning and causal inference.Questions naturally arise in this arena, including (1) How can human knowledge be incorporated with visual models? (2) How does human knowledge promote the performance of visual models? To address these problems, this book proposes a new framework for computer vision–computational knowledge vision.

Iris and Periocular Recognition using Deep Learning

1st Edition
June 12, 2024
Ajay Kumar
English
Paperback
9 7 8 0 4 4 3 2 7 3 1 8 6
eBook
9 7 8 0 4 4 3 2 7 3 1 9 3

Iris and Periocular Recognition using Deep Learning systematically explains the fundamental and most advanced techniques for ocular imprint-based human identification, with many applications in sectors such as healthcare, online education, e-business, metaverse, and entertainment. This is the first-ever book devoted to iris recognition that details cutting-edge techniques using deep neural networks. This book systematically introduces such algorithmic details with attractive illustrations, examples, experimental comparisons, and security analysis. It answers many fundamental questions about the most effective iris and periocular recognition techniques.

Intelligent Fractal-Based Image Analysis

1st Edition
May 27, 2024
Soumya Ranjan Nayak + 3 more
English
Paperback
9 7 8 0 4 4 3 1 8 4 6 8 0
eBook
9 7 8 0 4 4 3 1 8 4 6 9 7

Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applications as well as research findings on fractal graphics in engineering and science applications. The book aims to improve the exchange of ideas and coherence between various core computing methods and highlights the relevance of related application areas for advanced as well as novice-user application. The book presents core concepts, methodological aspects, and advanced feature opportunities, focusing on major, real-time applications in engineering and health science. It will appeal to researchers, data scientists, industry professionals, and graduate students.Fractals are infinite, complex patterns used in modeling physical and dynamic systems. Fractal theory research has increased across different fields of applications including engineering science, health science, and social science. Recent literature shows the vital role fractals play in digital image analysis, specifically in biomedical image processing. Fractal graphics is an interdisciplinary field that deals with how computers can be used to gain high-level understanding from digital images. Integrating artificial intelligence with fractal characteristics has resulted in new interdisciplinary research in the fields of pattern recognition and image processing analysis.

Machine Learning with Noisy Labels

1st Edition
February 23, 2024
Gustavo Carneiro
English
Paperback
9 7 8 0 4 4 3 1 5 4 4 1 6
eBook
9 7 8 0 4 4 3 1 5 4 4 2 3

Machine Learning and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine learning with noisy labels that is suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching, machine learning methods. Most of the modern machine learning models based on deep learning techniques depend on carefully curated and cleanly labeled training sets to be reliably trained and deployed. However, the expensive labeling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. This book defines the different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the effective use of noisy-label training sets, and explains the most accurate methods.

Engineering Simulation and its Applications

1st Edition
February 1, 2024
Xin-She Yang
English
Paperback
9 7 8 0 4 4 3 1 4 0 8 4 6
eBook
9 7 8 0 4 4 3 1 4 0 8 5 3

Engineering Simulation and its Applications: Algorithms and Numerical Methods covers the essential quantitative methods needed for engineering simulations, introducing optimization techniques that can be used in the design of systems to minimize cost and maximize efficiency. This book serves as a reference and textbook for courses such as engineering simulation, design optimization, mathematical modeling, numerical methods, data analysis, and engineering management. Diverse coverage of the various subject areas within the field puts the essential topics into a single book for easy access for graduates and senior undergraduates. It also serves as a reference book for lecturers and industrial practitioners.

High-Order Models in Semantic Image Segmentation

1st Edition
June 22, 2023
Ismail Ben Ayed
English
Hardback
9 7 8 0 1 2 8 0 5 3 2 0 1
eBook
9 7 8 0 1 2 8 0 9 2 2 9 3

High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging.

Visualization, Visual Analytics and Virtual Reality in Medicine

1st Edition
May 15, 2023
Bernhard Preim + 3 more
English
Paperback
9 7 8 0 1 2 8 2 2 9 6 2 0
eBook
9 7 8 0 1 2 8 2 3 1 0 6 7

Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understanding of actual user needs as well as technological possibilities. The book includes user research, for example, task and requirement analysis, visualization design and algorithmic ideas without going into the details of implementation. This reference will be suitable for researchers and students in visualization and visual analytics in medicine and healthcare, medical image analysis scientists and biomedical engineers in general. Visualization and visual analytics have become prevalent in public health and clinical medicine, medical flow visualization, multimodal medical visualization and virtual reality in medical education and rehabilitation. Relevant applications now include digital pathology, virtual anatomy and computer-assisted radiation treatment planning.

Biomedical Image Synthesis and Simulation

1st Edition
June 18, 2022
Ninon Burgos + 1 more
English
Paperback
9 7 8 0 1 2 8 2 4 3 4 9 7
eBook
9 7 8 0 1 2 8 2 4 3 5 0 3

Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future.

Advanced Methods and Deep Learning in Computer Vision

1st Edition
November 9, 2021
E. R. Davies + 1 more
English
Paperback
9 7 8 0 1 2 8 2 2 1 0 9 9
eBook
9 7 8 0 1 2 8 2 2 1 4 9 5

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students.

Deep Learning Models for Medical Imaging

1st Edition
September 7, 2021
KC Santosh + 2 more
English
Paperback
9 7 8 0 1 2 8 2 3 5 0 4 1
eBook
9 7 8 0 1 2 8 2 3 6 5 0 5

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow ‘with’ and ‘without’ transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientist... to industrialists.

Related subjects

Document and text processing

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health