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Computational Imaging and Analytics in Biomedical Engineering: Algorithms and Applications

Автор: Limpopo5 от 2024-03-22, 22:22:41
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Computational Imaging and Analytics in Biomedical Engineering: Algorithms and ApplicationsНазвание: Computational Imaging and Analytics in Biomedical Engineering: Algorithms and Applications
Автор: Т.R. Gаnеsh Ваbu, U. Sаrаvаnаkumаr, Ваlасhаndrа Раttаnаik
Издательство: Apple Academic Press/CRC Press
Серия: AAP Series on Digital Signal Processing, Computer Vision and Image Processing
Год: 2024
Страниц: 356
Язык: английский
Формат: pdf (true)
Размер: 27.9 MB

This new book focuses on mathematical and numerical methods for medical images and data. The book presents the various mathematical modeling techniques, numerical analysis, computing and computational techniques, and applications of machine learning for medical images and medical informatics. It also focuses on programming concepts using MATLAB and Python for medical image and signal analytics. The volume demonstrates the use of computational techniques and tools such as Machine Learning, deep neural networks, Artificial Intelligence (AI) and human-computer interaction ,fusion methods for CT and pet images, etc., for diagnosis of brain disorders, cervical cancer, lung disease, melanoma, atrial fibrillation and other circulatory issues, dental images, diabetes, and other medical issues. For further simplification and optimization, emerging techniques such as neural networks, Machine Learning, Deep Learning (DL), and Artificial Intelligence (AI) are adapted for feature extraction of medical images, medical image segmen­tation, and image-based bio-models. Image categorization, object identifica­tion, pattern recognition, and reasoning are just a few of the principles that are examined in medical imaging. By extracting meaningful patterns for a specific condition, they are employed in medical imaging to improve accu­racy. They also aid in the process of deciding on a course of action. Using Machine Learning to improve medical imaging is a trend that will continue in the future.

Artificial Intelligence and Machine Learning Techniques in Image Processing and Computer Vision

Автор: Limpopo5 от 2024-02-25, 16:45:18
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Artificial Intelligence and Machine Learning Techniques in Image Processing and Computer VisionНазвание: Artificial Intelligence and Machine Learning Techniques in Image Processing and Computer Vision
Автор: Каrm Vееr Аrуа, Сirо Rоdriguеz Rоdriguеz, Sаurаbh Singh
Издательство: Apple Academic Press/CRC Press
Год: 2024
Страниц: 341
Язык: английский
Формат: pdf (true)
Размер: 17.2 MB

This new volume provides in-depth and detailed knowledge about the latest research in image processing and Computer Vision techniques. Explaining the Machine Learning algorithms and models involved, the authors differentiate between the various algorithms available and how to choose which to use for the most precise results for a specific task involving certain constraints. The volume provides real-world examples to illustrate the concepts and methods. The authors discuss Machine Learning in healthcare systems for detection, diagnosis, classification, and segmentation. They also explore the diverse applications of image and video processing, including image colorization and restoration using Deep Learning, using Machine Learning to record the climate changes in over time with remote sensing, and more. Machine Learning (ML) algorithms learn from data by building adaptive mathematical models which can detect the pattern in the training data. One of the applications of Machine Learning is image processing and Computer Vision.

Image Processing and Machine Learning, Volume 1: Foundations of Image Processing

Автор: Limpopo5 от 2024-01-03, 09:55:15
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Image Processing and Machine Learning, Volume 1: Foundations of Image ProcessingНазвание: Image Processing and Machine Learning, Volume 1: Foundations of Image Processing
Автор: Еrik Сuеvаs, Аlmа Nауеli Rоdríguеz
Издательство: CRC Press
Год: 2024
Страниц: 225
Язык: английский
Формат: pdf (true)
Размер: 40.9 MB

Image processing and Machine Learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, Machine Learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. Our primary objective was to create a comprehensive textbook that serves as an invaluable resource for an image processing class. With this goal in mind, we carefully crafted a book that encompasses both the theoretical foundations and practical applications of the most prevalent image processing methods. From pixel operations to geometric transformations, spatial filtering to image segmentation, and edge detection to color image processing, we have meticulously covered a wide range of topics essential to understanding and working with images. Moreover, recognizing the increasing relevance of ML in image processing, we have incorporated fundamental ML concepts and their applications in this field. By introducing readers to these concepts, we aim to equip them with the necessary knowledge to leverage ML techniques for various image processing tasks. Volume 1 is organized in a way that allows readers to easily understand the goal of each chapter and reinforce their understanding through practical exercises using MATLAB programs.

Image Processing and Machine Learning, Volume 2: Advanced Topics in Image Analysis and Machine Learning

Автор: Limpopo5 от 2024-01-03, 09:15:57
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Image Processing and Machine Learning, Volume 2: Advanced Topics in Image Analysis and Machine LearningНазвание: Image Processing and Machine Learning, Volume 2: Advanced Topics in Image Analysis and Machine Learning
Автор: Еrik Сuеvаs, Аlmа Nауеli Rоdríguеz
Издательство: CRC Press
Год: 2024
Страниц: 239
Язык: английский
Формат: pdf (true)
Размер: 31.6 MB

Image processing and Machine Learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, Machine Learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this second installment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean shift algorithm, and the application of singular value decomposition for image compression. This second volume also incorporates several important Machine Learning techniques applied to image processing, building on the foundational knowledge introduced in Volume 1. Machine Learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data and make informed predictions or decisions without the need for explicit programming. ML finds extensive applications in various domains. For instance, in automation, ML algorithms can automate tasks that would otherwise rely on human intervention, thereby reducing errors and enhancing overall efficiency. Predictive analytics is another area where ML plays a crucial role. By analyzing vast datasets, ML models can detect patterns and make predictions, facilitating applications such as stock market analysis, fraud detection, and customer behavior analysis. We have observed that students grasp the material more effectively when they have access to code that they can manipulate and experiment with. In line with this, our book utilizes MATLAB as the programming language for implementing the systems.

MATLAB Image Acquisition Toolbox User’s Guide (R2023b)

Автор: Limpopo5 от 2023-12-25, 16:57:57
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MATLAB Image Acquisition Toolbox User’s Guide (R2023b)Название: MATLAB Image Acquisition Toolbox User’s Guide (R2023b)
Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: September 2023
Страниц: 666
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Acquire images and video from industry-standard hardware. Image Acquisition Toolbox provides functions and blocks for connecting cameras to MATLAB and Simulink. It includes a MATLAB app that lets you interactively detect and configure hardware properties. You can then generate equivalent MATLAB code to automate your acquisition in future sessions. The toolbox enables acquisition modes such as processing in-the-loop, hardware triggering, background acquisition, and synchronizing acquisition across multiple devices. Image Acquisition Toolbox supports all major standards and hardware vendors, including USB3 Vision, GigE Vision, and GenICam GenTL. You can connect to machine vision cameras and frame grabbers, as well as high-end scientific and industrial devices.

Classical Signal Processing and Non-Classical Signal Processing: The Rhythm of Signals

Автор: Limpopo5 от 2023-10-16, 04:21:07
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Classical Signal Processing and Non-Classical Signal Processing: The Rhythm of SignalsНазвание: Classical Signal Processing and Non-Classical Signal Processing: The Rhythm of Signals
Автор: Аttарhоngsе Тараrugssаnаgоrn
Издательство: Cambridge Scholars Publishing
Год: 2023
Страниц: 229
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Expertly unraveling the mysteries and allure of signals, this book explores their profound impact on modern life. From classical techniques to cutting-edge advancements, this comprehensive exploration delves into fundamental concepts such as amplitude, frequency, and phase. With meticulous research and insightful analysis, the author guides readers through topics like Fourier analysis, sampling, quantization, and signal filtering.The book highlights the dynamic relationship between time and frequency domains, statistical signal processing techniques, and the fascinating realm of non-classical signal processing, including wavelet transforms and compressed sensing, and explores diverse applications in audio, speech, image and video processing, biomedical analysis, communications, and sensor fusion.Highlighting emerging trends and future directions, the book illuminates the challenges, opportunities, and potential breakthroughs in signal processing research. Classical signal processing is a branch of electrical engineering and applied mathematics that deals with the analysis, modification, and synthesis of signals. It encompasses various techniques for transforming, filtering, and analyzing signals to extract useful information or enhance their quality. The main goal of classical signal processing is to improve the performance and efficiency of systems that rely on signals, such as communication systems, audio and video processing, and control systems.

Applications of Optimization and Machine Learning in Image Processing and IoT

Автор: Limpopo5 от 2023-09-30, 15:40:18
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Applications of Optimization and Machine Learning in Image Processing and IoTНазвание: Applications of Optimization and Machine Learning in Image Processing and IoT
Автор: Nidhi Gupta
Издательство: CRC Press
Год: 2024
Страниц: 236
Язык: английский
Формат: pdf (true)
Размер: 10.8 MB

This book presents state-of-the-art optimization algorithms followed by Internet of Things (IoT) fundamentals. The applications of machine learning and IoT are explored, with topics including optimization, algorithms and Machine Learning in image processing and IoT. Applications of Optimization and Machine Learning in Image Processing and IoT is a complete reference source, providing the latest research findings and solutions for optimization and machine learning algorithms. The chapters examine and discuss the fields of Machine Learning, IoT and image processing. The field of Computer Science with the fastest growth right now is Machine Learning, which has applications in fields as varied as marketing, health-care, production, cybersecurity and mobility. Three elements are readily available and combined: (1) faster and more potent part of a computer, like multiple cores and broad sense GPU; (2) a computer program that utilizes these computational structures; and (3) essentially unlimited training data sets for a certain issue, like digital photos, digitalized files. The two stages of a computer-vision-based Machine Learning process are feature extraction and classification. Additional Machine Learning models, including ANN, CNN, RNN and others, can be applied for system training and optimization.

Design for Embedded Image Processing on FPGAs, 2nd edition

Автор: admin от 2023-09-02, 15:14:32
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Design for Embedded Image Processing on FPGAs, 2nd editionНазвание: Design for Embedded Image Processing on FPGAs, 2nd edition
Автор: Donald G. Bailey
Издательство: Wiley
Год: 2024
Страниц: 499
Язык: английский
Формат: pdf (true)
Размер: 25.8 MB

Design for Embedded Image Processing on FPGAs. Bridge the gap between software and hardware with this foundational design reference. Field-programmable gate arrays (FPGAs) are integrated circuits designed so that configuration can take place. Circuits of this kind play an integral role in processing images, with FPGAs increasingly embedded in digital cameras and other devices that produce visual data outputs for subsequent realization and compression. These uses of FPGAs require specific design processes designed to mediate smoothly between hardware and processing algorithm. Design for Embedded Image Processing on FPGAs provides a comprehensive overview of these processes and their applications in embedded image processing. Beginning with an overview of image processing and its core principles, this book discusses specific design and computation techniques, with a smooth progression from the foundations of the field to its advanced principles.

Robotics, Vision and Control: Fundamental Algorithms in Python, 3rd Edition

Автор: Limpopo5 от 2023-05-12, 08:15:47
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Robotics, Vision and Control: Fundamental Algorithms in Python, 3rd EditionНазвание: Robotics, Vision and Control: Fundamental Algorithms in Python, 3rd Edition
Автор: Реtеr Соrkе
Издательство: Springer
Год: 2023
Страниц: 835
Язык: английский
Формат: pdf (true)
Размер: 54.5 MB

This textbook provides a comprehensive, but tutorial, introduction to robotics, computer vision, and control. It is written in a light but informative conversational style, weaving text, figures, mathematics, and lines of code into a narrative that covers robotics and computer vision?separately, and together as robotic vision. Over 1600 code examples show how complex problems can be decomposed and solved using just a few simple lines of code. This edition is based on Python and is accompanied by fully open-source Python-based Toolboxes for robotics and machine vision. The new Toolboxes enable the reader to easily bring the algorithmic concepts into practice and work with real, non-trivial, problems on a broad range of computing platforms. For the beginning student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used. The code can also be the starting point for new work, for practitioners, students, or researchers, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. Python, combined with popular packages such as NumPy, SciPy and Matplotlib provides a powerful interactive mathematical software environment that makes linear algebra, data analysis and high-quality graphics a breeze.

Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods

Автор: Limpopo5 от 2023-05-06, 18:57:34
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Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning MethodsНазвание: Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
Автор: Kemal Polat, Saban Ozturk
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 303
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Deep learning (DL), which has transformed several industries over the past ten years, has astounded practitioners with its amazing performance across nearly all application domains. The following fundamental ideas related to DL can be arranged in order of breadth to depth: (1) the brain-inspired artificial neural network (ANN) algorithm; (2) Machine Learning (ML) methods that allow machines to learn from examples without having to be explicitly programmed; and (3) Artificial Intelligence (AI), a theory that aims to artificially mimic the intelligent behavior of beings found in nature.


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