Название: Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem
Автор: Аlех Кhаng, Vugаr Аbdullауеv, Оlеnа Нrуbiuk
Издательство: CRC Press
Год: 2024
Страниц: 458
Язык: английский
Формат: pdf (true)
Размер: 19.2 MB

This book examines computer vision and IoT-integrated technologies used by medical professionals in decision-making, for sustainable development in a healthcare ecosystem, and to better serve patients and stakeholders. It looks at the methodologies, technologies, models, frameworks, and practices necessary to resolve the challenging issues associated with leveraging the emerging technologies driving the medical field. The chapters discuss machine vision, AI-driven computer vision, Machine Learning, Deep Learning, AI-integrated IoT technology, Data Science, blockchain, AR/VR technology, cloud data, and cybersecurity techniques in designing and implementing a smart healthcare infrastructure in the era of the Industrial Revolution 4.0. Techniques are applied to the detection, diagnosis, and monitoring of a wide range of health issues. Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem targets a mixed audience of students, engineers, researchers, academics, and professionals who are researching and working in the field of medical and healthcare industries from different environments and countries.

Название: Measurements and Instrumentation for Machine Vision
Автор: Оlеg Sеrgiуеnkо, Wеndу Flоrеs-Fuеntеs
Издательство: CRC Press
Год: 2024
Страниц: 466
Язык: английский
Формат: pdf (true)
Размер: 42.3 MB

A comprehensive reference book that addresses the field of machine vision and its significance in cyber-physical systems. It explores the multidisciplinary nature of machine vision, involving electronic and mechatronic devices, Artificial Intelligence algorithms, embedded systems, control systems, robotics, interconnectivity, Data Science, and cloud computing. The book aims to provide advanced students, early career researchers, and established scholars with state-of-the-art knowledge and novel content related to the implementation of machine vision in engineering, scientific knowledge, and technological innovation. The chapters of the book delve into various topics and applications within the realm of machine vision. They cover areas such as camera and inertial measurement unit calibration, technical vision systems for human detection, design and evaluation of support systems using neural networks, UV sensing in contemporary applications, fiber Bragg grating arrays for medical diagnosis, color model creation for terrain recognition by robots, navigation systems for aircraft, object classification in infrared images, feature selection for vehicle/non-vehicle classification, visualization of sedimentation in extreme conditions, quality estimation of tea using machine vision, image dataset augmentation techniques, machine vision for astronomical images, etc. The use of current technology requires measuring essential attributes from objects, health data, dimensions of a surface and weather, to mention some. These are necessary to do breakthrough innovations in a wide range of fields. The Artificial Intelligence (AI) field is one of them. This field is aimed at the research for imitating human abilities, above all, how they learn. AI can be divided into two main branches such as Machine Learning and Deep Learning.

Название: Computer Vision: Object Detection In Adversarial Vision
Автор: Мrinаl Каnti Вhоwmik
Издательство: CRC Press
Год: 2024
Страниц: 209
Язык: английский
Формат: pdf (true)
Размер: 81.2 MB

This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and Deep Learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of objects, applications of object detection, and real-world challenges faced by the research community for object detection task. The book addresses various real-world degradations and artifacts for the object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the preeminent researchers in the field, addressing not only the background of visibility enhancement but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative real-world challenges. Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging real-world conditions. Contains various hands-on practical examples and a tutorial for solving object detection problems using Python.

Название: Neural Network Computer Vision with OpenCV 5: Build computer vision solutions using Python and DNN module
Автор: Gорi Кrishnа Nuti
Издательство: BPB Publications
Год: 2024
Страниц: 307
Язык: английский
Формат: epub (true)
Размер: 31.5 MB

Unlocking computer vision with Python and OpenCV. Neural Network Computer Vision with OpenCV equips you with professional skills and knowledge to build intelligent vision systems using OpenCV. It creates a sequential pathway for understanding morphological operations, edge and corner detection, object localization, image classification, segmentation, and advanced applications like face detection and recognition, and optical character recognition. This book offers a practical roadmap to explore the nuances of image processing with detailed discussions on each topic, supported by hands-on Python code examples. The readers will learn the basics of neural networks, Deep Learning and CNNs by using Deep Learning frameworks like Keras, Tensorflow, PyTorch, Caffe etc. They will be able to utilize OpenCV DNN module to classify images by using models like Inception V3, Resnet 101, Mobilenet V2. Moreover, the book will help to successfully Implement object detection using YOLOv3, SSD and R-CNN models. The character detection and recognition models are also covered in depth with code examples. You will gain a deeper understanding of how these techniques impact real-world scenarios and learn to harness the potential of Python and OpenCV to solve complex problems.

Название: Computer Vision: Three-dimensional Reconstruction Techniques
Автор: Аndrеа Fusiеllо
Издательство: Springer
Год: 2024
Страниц: 348
Язык: английский
Формат: pdf (true), epub
Размер: 40.4 MB

From facial recognition to self-driving cars, the applications of computer vision are vast and ever-expanding. Geometry plays a fundamental role in this discipline, providing the necessary mathematical framework to understand the underlying principles of how we perceive and interpret visual information in the world around us. This text explores the theories and computational techniques used to determine the geometric properties of solid objects through images. It covers the basic concepts and provides the necessary mathematical background for more advanced studies. The book is divided into clear and concise chapters covering a wide range of topics including image formation, camera models, feature detection and 3D reconstruction. Each chapter includes detailed explanations of the theory as well as practical examples to help the reader understand and apply the concepts presented. Deep Learning has brought undeniable successes and some breakthroughs in image recognition and scene description. It is nevertheless true that geometric Computer Vision remains a fundamental field. Given the impressive state-of-the-art and the rapid pace of progress in Deep Learning, it would be of course risky to rule out the possibility that the solution to many geometric vision problems, for instance reconstructing 3D structure from multiple images, can be learned from millions of examples. Yet we believe that a principled, approach that obtains the geometric structure of what we see through applied mathematics provides more insight. We would also go as far as suggesting that, in the end, such an approach can be even more fun to study and implement. The book has been written with the intention of being used as a primary resource for students on university courses in Computer Vision, particularly final year undergraduate or postgraduate Computer Science or engineering courses. To aid the reader in implementation, most of the methods discussed in the book are accompanied by a Matlab listing and the sources are available on Github.

Название: Цифровая обработка изображений в OpenCV. Практикум: учебное пособие для вузов
Автор: Maтвeeв A.И.
Издательство: Лань
Год: 2022
Страниц: 103
Язык: русский
Формат: pdf
Размер: 12.1 MB

Компьютерное зрение находит все большее практическое применение в различных сферах деятельности человека. Дисциплина изучает алгоритмы цифровой обработки изображений, занимается реализацией на практике предложенных решений. В методическом пособии предлагаются задания, с помощью которых можно овладеть практическими навыками работы с библиотекой компьютерного зрения и машинного обучения с открытым исходным кодом OpenCV на языке Python. Учебное пособие предназначено для студентов бакалавров и магистров по таким специальностям, как мехатроника и робототехника, вычислительная техника, управление в технических системах, автоматизация технологических процессов в производстве. В учебном пособии даны задания, предназначенные для закрепления теоретических знаний по цифровой обработке изображений в OpenCV.

Название: Компьютерное зрение. Передовые методы и глубокое обучение
Автор: Poй Дэвиc, Mэтью Tepк
Издательство: ДМК Пресс
Год: 2022
Страниц: 692
Язык: русский
Формат: pdf
Размер: 42,4 MB

Эта книга рассказывает о передовых методах компьютерного зрения. Показано, как искусственный интеллект обнаруживает признаки и объекты, на каких данных он обучается, на чем основано распознавание лиц и действий, отслеживание аномалий. Особое внимание уделяется методам глубокого обучения. Все ключевые принципы проиллюстрированы примерами из реальной практики. Книга адресована исследователям и практикам в области передовых методов компьютерного зрения, а также тем, кто изучает эту технологию самостоятельно или в рамках вузовского курса. Миновало почти десятилетие с тех пор, как произошел прорыв в разработке и применении глубоких нейронных сетей (deep neural network, DNN), и их последующий прогресс можно почти без преувеличения назвать выдающимся.

Название: Active Lighting and Its Application for Computer Vision: 40 Years of History of Active Lighting Techniques
Автор: Katsushi Ikeuchi, Yasuyuki Matsushita, Ryusuke Sagawa
Издательство: Springer
Год: 2020
Страниц: 309
Язык: английский
Формат: pdf (true)
Размер: 18.4 MB

Computer vision entails both passive and active illumination techniques. Whereas passive techniques observe the scene statically and analyse it as is, by contrast active techniques give the scene some actions and try to facilitate the analysis. In particular, active illumination techniques project specific light, for which the characteristics are known beforehand, to a target scene to enable stable and accurate analysis of the scene. Notably, traditional passive techniques have a fundamental limitation: The external world surrounding us is three-dimensional; the image projected on a retina or an imaging device is two-dimensional (That is, reduction of one dimension has occurred). Active illumination techniques compensate for the dimensional reduction by actively controlling the illumination.

Для сайта: BooksKeeper.ru
Название: Компьютерное зрение
Автор: Клетте Рейнхард
Год: 2019
Жанр: компьютерная, программирование
Издательство: ДМК Пресс
Язык: Русский
Формат: djvu
Качество: Отсканированные страницы + слой распознанного текста
Страниц: 508
Размер: 48 MB

В данной книге рассмотрены основные аспекты компьютерного зрения: обработка и анализ изображений, анализ плотного движения, сегментация изображений, работа с камерами, трехмерная реконструкция, сопоставление стереоизображений, обнаружение объектов и др. Материал дополняется историческими справками, рекомендациями по дальнейшему чтению и сведениями о рассматриваемых математических понятиях. В конце каждой главы имеются проверенные на практике упражнения и вопросы на понимание материала.
Издание предназначено широкому кругу специалистов по анализу данных и изображений, а также может использоваться в качестве учебника для студентов старших курсов и для самообразования.