Image Processing And Machine Learning Volume 1
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Image Processing And Machine Learning Volume 1
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Author : Erik Cuevas
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2024-02
Image Processing And Machine Learning Volume 1 written by Erik Cuevas and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02 with Computers categories.
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.
Image Processing And Machine Learning Volume 2
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Author : Erik Cuevas
language : en
Publisher: CRC Press
Release Date : 2024-02-16
Image Processing And Machine Learning Volume 2 written by Erik Cuevas and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-16 with Computers categories.
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. Written with instructors and students of image processing in mind, this book’s intuitive organization also contains appeal for app developers and engineers.
Handbook Of Image Processing And Computer Vision
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Author : Arcangelo Distante
language : en
Publisher: Springer Nature
Release Date : 2020-05-28
Handbook Of Image Processing And Computer Vision written by Arcangelo Distante and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-28 with Computers categories.
Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Volume 1 (From Energy to Image) examines the formation, properties, and enhancement of a digital image. Topics and features: • Describes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy • Covers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal • Discusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image • Reviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration • Presents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest • Examines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration • Provides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks This all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT/software and electronics industries involved in machine vision, imaging, and artificial intelligence. Dr. Cosimo Distante is a Research Scientist in Computer Vision and Pattern Recognition in the Institute of Applied Sciences and Intelligent Systems (ISAI) at the Italian National Research Council (CNR). Dr. Arcangelo Distante is a researcher and the former Director of the Institute of Intelligent Systems for Automation (ISSIA) at the CNR. His research interests are in the fields of Computer Vision, Pattern Recognition, Machine Learning, and Neural Computation.
Deep Learning For Image Processing Applications
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Author : D.J. Hemanth
language : en
Publisher: IOS Press
Release Date : 2017-12
Deep Learning For Image Processing Applications written by D.J. Hemanth and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12 with Computers categories.
Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.
Artificial Intelligence And Machine Learning A Precise Book To Learn Basics
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Author : pc
language : en
Publisher: by Mocktime Publication
Release Date :
Artificial Intelligence And Machine Learning A Precise Book To Learn Basics written by pc and has been published by by Mocktime Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Artificial Intelligence and Machine Learning - A Precise Book to Learn Basics Table of Contents 1. Introduction to Artificial Intelligence and Machine Learning 1.1 What is Artificial Intelligence? 1.2 The Evolution of Artificial Intelligence 1.3 What is Machine Learning? 1.4 How Machine Learning Differs from Traditional Programming 1.5 The Importance of Artificial Intelligence and Machine Learning 2. Foundations of Machine Learning 2.1 Supervised Learning 2.1.1 Linear Regression 2.1.2 Logistic Regression 2.1.3 Decision Trees 2.2 Unsupervised Learning 2.2.1 Clustering 2.2.2 Dimensionality Reduction 2.3 Reinforcement Learning 2.3.1 Markov Decision Process 2.3.2 Q-Learning 3. Neural Networks and Deep Learning 3.1 Introduction to Neural Networks 3.2 Artificial Neural Networks 3.2.1 The Perceptron 3.2.2 Multi-Layer Perceptron 3.3 Convolutional Neural Networks 3.4 Recurrent Neural Networks 3.5 Generative Adversarial Networks 4. Natural Language Processing 4.1 Introduction to Natural Language Processing 4.2 Preprocessing and Text Representation 4.3 Sentiment Analysis 4.4 Named Entity Recognition 4.5 Text Summarization 5. Computer Vision 5.1 Introduction to Computer Vision 5.2 Image Processing 5.3 Object Detection 5.4 Image Segmentation 5.5 Face Recognition 6. Reinforcement Learning Applications 6.1 Reinforcement Learning in Robotics 6.2 Reinforcement Learning in Games 6.3 Reinforcement Learning in Finance 6.4 Reinforcement Learning in Healthcare 7. Ethics and Social Implications of Artificial Intelligence 7.1 Bias in Artificial Intelligence 7.2 The Future of Work 7.3 Privacy and Security 7.4 The Impact of AI on Society 8. Machine Learning Infrastructure 8.1 Cloud Infrastructure for Machine Learning 8.2 Distributed Machine Learning 8.3 DevOps for Machine Learning 9. Machine Learning Tools 9.1 Introduction to Machine Learning Tools 9.2 Python Libraries for Machine Learning 9.3 TensorFlow 9.4 Keras 9.5 PyTorch 10. Building and Deploying Machine Learning Models 10.1 Building a Machine Learning Model 10.2 Hyperparameter Tuning 10.3 Model Evaluation 10.4 Deployment Considerations 11. Time Series Analysis and Forecasting 11.1 Introduction to Time Series Analysis 11.2 ARIMA 11.3 Exponential Smoothing 11.4 Deep Learning for Time Series 12. Bayesian Machine Learning 12.1 Introduction to Bayesian Machine Learning 12.2 Bayesian Regression 12.3 Bayesian Classification 12.4 Bayesian Model Averaging 13. Anomaly Detection 13.1 Introduction to Anomaly Detection 13.2 Unsupervised Anomaly Detection 13.3 Supervised Anomaly Detection 13.4 Deep Learning for Anomaly Detection 14. Machine Learning in Healthcare 14.1 Introduction to Machine Learning in Healthcare 14.2 Electronic Health Records 14.3 Medical Image Analysis 14.4 Personalized Medicine 15. Recommender Systems 15.1 Introduction to Recommender Systems 15.2 Collaborative Filtering 15.3 Content-Based Filtering 15.4 Hybrid Recommender Systems 16. Transfer Learning 16.1 Introduction to Transfer Learning 16.2 Fine-Tuning 16.3 Domain Adaptation 16.4 Multi-Task Learning 17. Deep Reinforcement Learning 17.1 Introduction to Deep Reinforcement Learning 17.2 Deep Q-Networks 17.3 Actor-Critic Methods 17.4 Deep Reinforcement Learning Applications 18. Adversarial Machine Learning 18.1 Introduction to Adversarial Machine Learning 18.2 Adversarial Attacks 18.3 Adversarial Defenses 18.4 Adversarial Machine Learning Applications 19. Quantum Machine Learning 19.1 Introduction to Quantum Computing 19.2 Quantum Machine Learning 19.3 Quantum Computing Hardware 19.4 Quantum Machine Learning Applications 20. Machine Learning in Cybersecurity 20.1 Introduction to Machine Learning in Cybersecurity 20.2 Intrusion Detection 20.3 Malware Detection 20.4 Network Traffic Analysis 21. Future Directions in Artificial Intelligence and Machine Learning 21.1 Reinforcement Learning in Real-World Applications 21.2 Explainable Artificial Intelligence 21.3 Quantum Machine Learning 21.4 Autonomous Systems 22. Conclusion 22.1 Summary 22.2 Key Takeaways 22.3 Future Directions 22.4 Call to Action
Practical Machine Learning And Image Processing
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Author : Himanshu Singh
language : en
Publisher: Apress
Release Date : 2019-02-26
Practical Machine Learning And Image Processing written by Himanshu Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-26 with Computers categories.
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will LearnDiscover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
Machine Learning For Audio Image And Video Analysis
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Author : Francesco Camastra
language : en
Publisher: Springer
Release Date : 2015-07-21
Machine Learning For Audio Image And Video Analysis written by Francesco Camastra and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-21 with Computers categories.
This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.
Image Processing Masterclass With Python
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Author : Sandipan Dey
language : en
Publisher: BPB Publications
Release Date : 2021-03-10
Image Processing Masterclass With Python written by Sandipan Dey and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-10 with Computers categories.
Over 50 problems solved with classical algorithms + ML / DL models KEY FEATURESÊ _ Problem-driven approach to practice image processing.Ê _ Practical usage of popular Python libraries: Numpy, Scipy, scikit-image, PIL and SimpleITK. _ End-to-end demonstration of popular facial image processing challenges using MTCNN and MicrosoftÕs Cognitive Vision APIs. Ê DESCRIPTIONÊ This book starts with basic Image Processing and manipulation problems and demonstrates how to solve them with popular Python libraries and modules. It then concentrates on problems based on Geometric image transformations and problems to be solved with Image hashing.Ê Next, the book focuses on solving problems based on Sampling, Convolution, Discrete Fourier transform, Frequency domain filtering and image restoration with deconvolution. It also aims at solving Image enhancement problems using differentÊ algorithms such as spatial filters and create a super resolution image using SRGAN. Finally, it explores popular facial image processing problems and solves them with Machine learning and Deep learning models using popular python ML / DL libraries. WHAT YOU WILL LEARNÊÊ _ Develop strong grip on the fundamentals of Image Processing and Image Manipulation. _ Solve popular Image Processing problems using Machine Learning and Deep Learning models. _ Working knowledge on Python libraries including numpy, scipyÊ and scikit-image. _ Use popular Python Machine Learning packages such as scikit-learn, Keras and pytorch. _ Live implementation of Facial Image Processing techniques such as Face Detection / Recognition / Parsing dlib and MTCNN. WHO THIS BOOK IS FORÊÊÊ This book is designed specially for computer vision users, machine learning engineers, image processing experts who are looking for solving modern image processing/computer vision challenges. TABLE OF CONTENTS 1. Chapter 1: Basic Image & Video Processing 2. Chapter 2: More Image Transformation and Manipulation 3. Chapter 3: Sampling, Convolution and Discrete Fourier Transform 4. Chapter 4: Discrete Cosine / Wavelet Transform and Deconvolution 5. Chapter 5: Image Enhancement 6. Chapter 6: More Image Enhancement 7. Chapter 7: Facel Image Processing
Image Processing And Machine Learning Volume 1
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Author : Erik Cuevas
language : en
Publisher: CRC Press
Release Date : 2024-02-16
Image Processing And Machine Learning Volume 1 written by Erik Cuevas and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-16 with Computers categories.
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. It provides a solid foundation for readers interested in understanding the core principles and practical applications of image processing, establishing the essential groundwork necessary for further explorations covered in Volume 2. Written with instructors and students of image processing in mind, this book’s intuitive organization also contains appeal for app developers and engineers.
Applied Computer Vision And Image Processing
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Author : Brijesh Iyer
language : en
Publisher: Springer Nature
Release Date : 2020-07-28
Applied Computer Vision And Image Processing written by Brijesh Iyer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-28 with Technology & Engineering categories.
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.