Advanced Methods and Deep Learning in Computer Vision Book

Advanced Methods and Deep Learning in Computer Vision


  • Author : E. R. Davies
  • Publisher : Academic Press
  • Release Date : 2021-11-09
  • Genre: Computers
  • Pages : 582
  • ISBN 10 : 9780128221495

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Advanced Methods and Deep Learning in Computer Vision Excerpt :

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. Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field Illustrates principles with modern, real-world applications Suitable for self-learning or as a text for graduate courses

Machine Learning for OpenCV Book
Score: 5
From 2 Ratings

Machine Learning for OpenCV


  • Author : Michael Beyeler
  • Publisher : Packt Publishing Ltd
  • Release Date : 2017-07-14
  • Genre: Computers
  • Pages : 382
  • ISBN 10 : 9781783980291

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Machine Learning for OpenCV Excerpt :

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book Load, store, edit, and visualize data using OpenCV and Python Grasp the fundamental concepts of classification, regression, and clustering Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn Explore and make effective use of OpenCV's machine learning module Learn deep learning for computer vision with Python Master linear regression and regularization techniques Classify objects such as flower species, handwritten digits, and pedestrians Explore the effective use of support vector machines, boosted decision trees, and random forests Get acquainted with neural networks and Deep Learning to address real-world problems Discover hidden structures in your data using k-means clustering Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the esse

Deep Learning for Computer Vision Book
Score: 4
From 1 Ratings

Deep Learning for Computer Vision


  • Author : Rajalingappaa Shanmugamani
  • Publisher : Packt Publishing Ltd
  • Release Date : 2018-01-23
  • Genre: Computers
  • Pages : 310
  • ISBN 10 : 9781788293358

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Deep Learning for Computer Vision Excerpt :

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book

Mastering Computer Vision with TensorFlow 2 x Book

Mastering Computer Vision with TensorFlow 2 x


  • Author : Krishnendu Kar
  • Publisher : Packt Publishing Ltd
  • Release Date : 2020-05-15
  • Genre: Computers
  • Pages : 430
  • ISBN 10 : 9781838826932

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Mastering Computer Vision with TensorFlow 2 x Excerpt :

Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key Features Gain a fundamental understanding of advanced computer vision and neural network models in use today Cover tasks such as low-level vision, image classification, and object detection Develop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkit Book Description Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks. What you will learn Explore methods of feature extraction and image retrieval and visualize different layers of the neural network model Use TensorFlow for various visual search methods for real-wo

Modern Deep Learning and Advanced Computer Vision Book

Modern Deep Learning and Advanced Computer Vision


  • Author : J. Nedumaan
  • Publisher : Unknown
  • Release Date : 2019-12-08
  • Genre: Uncategoriezed
  • Pages : 531
  • ISBN 10 : 1708798641

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Modern Deep Learning and Advanced Computer Vision Excerpt :

Computer vision has enormous progress in modern times. Deep learning has driven and inferred a range of computer vision problems, such as object detection and recognition, face detection and recognition, motion tracking and estimation, transfer learning, action recognition, image segmentation, semantic segmentation, robotic vision. The chapters in this book are persuaded towards the applications of advanced computer vision using modern deep learning techniques. The authors trust in making the readers with more interesting illustrations in understanding the concepts of deep learning and computer vision at a simpler perspective approach.

Computer Vision Book

Computer Vision


  • Author : Simon J. D. Prince
  • Publisher : Cambridge University Press
  • Release Date : 2012-06-18
  • Genre: Computers
  • Pages : 580
  • ISBN 10 : 9781107011793

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Computer Vision Excerpt :

A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.

Deep Learning in Computer Vision Book
Score: 5
From 1 Ratings

Deep Learning in Computer Vision


  • Author : Mahmoud Hassaballah
  • Publisher : CRC Press
  • Release Date : 2020-03-23
  • Genre: Computers
  • Pages : 322
  • ISBN 10 : 9781351003803

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Deep Learning in Computer Vision Excerpt :

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Machine Learning for OpenCV   Advanced Methods and Deep Learning Book

Machine Learning for OpenCV Advanced Methods and Deep Learning


  • Author : Michael Beyeler
  • Publisher : Unknown
  • Release Date : 2018
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : OCLC:1137386382

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Machine Learning for OpenCV Advanced Methods and Deep Learning Excerpt :

A practical introduction to the world of machine learning and image processing using OpenCV and Python About This Video Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide Grasp the advanced concepts of bootstrapping, boosting, voting, and bagging Evaluate, compare, and choose the right algorithm for any task Load, store, edit and visualize data using OpenCV and Python In Detail Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains. This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems. All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Advanced-Methods-and-Deep-Learning The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems.

Practical Machine Learning for Computer Vision Book

Practical Machine Learning for Computer Vision


  • Author : Valliappa Lakshmanan
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2021-07-21
  • Genre: Computers
  • Pages : 482
  • ISBN 10 : 9781098102333

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Practical Machine Learning for Computer Vision Excerpt :

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Deep Learning and Convolutional Neural Networks for Medical Image Computing Book

Deep Learning and Convolutional Neural Networks for Medical Image Computing


  • Author : Le Lu
  • Publisher : Springer
  • Release Date : 2017-07-12
  • Genre: Computers
  • Pages : 326
  • ISBN 10 : 9783319429991

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Deep Learning and Convolutional Neural Networks for Medical Image Computing Excerpt :

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Advanced Topics in Computer Vision Book

Advanced Topics in Computer Vision


  • Author : Giovanni Maria Farinella
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-09-24
  • Genre: Computers
  • Pages : 433
  • ISBN 10 : 9781447155201

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Advanced Topics in Computer Vision Excerpt :

This book presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of reconstruction, registration, and recognition. The text provides an overview of challenging areas and descriptions of novel algorithms. Features: investigates visual features, trajectory features, and stereo matching; reviews the main challenges of semi-supervised object recognition, and a novel method for human action categorization; presents a framework for the visual localization of MAVs, and for the use of moment constraints in convex shape optimization; examines solutions to the co-recognition problem, and distance-based classifiers for large-scale image classification; describes how the four-color theorem can be used for solving MRF problems; introduces a Bayesian generative model for understanding indoor environments, and a boosting approach for generalizing the k-NN rule; discusses the issue of scene-specific object detection, and an approach for making temporal super resolution video.

Deep Learning for Computer Vision Book

Deep Learning for Computer Vision


  • Author : Jason Brownlee
  • Publisher : Machine Learning Mastery
  • Release Date : 2019-04-04
  • Genre: Computers
  • Pages : 563
  • ISBN 10 : 978186723xxxx

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Deep Learning for Computer Vision Excerpt :

Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Advanced Deep Learning with Keras Book

Advanced Deep Learning with Keras


  • Author : Rowel Atienza
  • Publisher : Packt Publishing Ltd
  • Release Date : 2018-10-31
  • Genre: Computers
  • Pages : 368
  • ISBN 10 : 9781788624534

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Advanced Deep Learning with Keras Excerpt :

A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs, and Deep Reinforcement Learning A wide study of GANs, including Improved GANs, Cross-Domain GANs and Disentangled Representation GANs Book Description Recent developments in deep learning, including GANs, Variational Autoencoders, and Deep Reinforcement Learning, are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and Tensorflow, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. You then learn all about Generative Adversarial Networks (GANs), and how they can open new levels of AI performance. Variational AutoEncoders (VAEs) are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, wh

Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition  Emerging Research and Opportunities Book

Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition Emerging Research and Opportunities


  • Author : Chakraborty, Shouvik
  • Publisher : IGI Global
  • Release Date : 2020-03-13
  • Genre: Computers
  • Pages : 271
  • ISBN 10 : 9781799827382

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Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition Emerging Research and Opportunities Excerpt :

Computer vision and object recognition are two technological methods that are frequently used in various professional disciplines. In order to maintain high levels of quality and accuracy of services in these sectors, continuous enhancements and improvements are needed. The implementation of artificial intelligence and machine learning has assisted in the development of digital imaging, yet proper research on the applications of these advancing technologies is lacking. Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities explores the theoretical and practical aspects of modern advancements in digital image analysis and object detection as well as its applications within healthcare, security, and engineering fields. Featuring coverage on a broad range of topics such as disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers, academicians, practitioners, scientists, industry professionals, scholars, and students seeking research on the current developments in object recognition using artificial intelligence.

Advanced Methods for Human Biometrics Book

Advanced Methods for Human Biometrics


  • Author : Nabil Derbel
  • Publisher : Springer Nature
  • Release Date : 2022-07-01
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : 9783030819828

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Advanced Methods for Human Biometrics Excerpt :