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
  • Total Read : 67
  • File Size : 10,7 Mb

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Advanced Methods and Deep Learning in Computer Vision Summary:

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

Advanced Methods and Deep Learning in Computer Vision Book

Advanced Methods and Deep Learning in Computer Vision


  • Author : E. R. Davies
  • Publisher : Elsevier
  • Release Date : 2021-11-12
  • Genre: Computers
  • Pages : 582
  • ISBN 10 : 9780128221099
  • Total Read : 72
  • File Size : 18,7 Mb

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Advanced Methods and Deep Learning in Computer Vision Summary:

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine learning 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, 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. Key Features . Provides an important reference on deep learning and advanced computer vision methods that were created by leaders in the field . Illustrates principles with modern, real-world applications . Suitable for self-learning or as a text for graduate courses About the Editors Roy Davies is Emeritus Professor of Machine Vision at Royal Holloway, University of London. He has worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests include automated visual inspection, surveillance, vehicle guidance, crime detection, and neural networks. He has published more than 200 papers, and three books. The first, published in 1990, has been widely used internationally for more than 25 years: in 2017 it came out in a fifth edition entitled Computer Vision, Principles, Algorithms, Applications, Learning. Roy holds a DSc at the University of London and has been awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition. Matthew Turk is the President of th

Computer Vision Book

Computer Vision


  • Author : Simon J. D. Prince
  • Publisher : Cambridge University Press
  • Release Date : 2012-06-18
  • Genre: Computers
  • Pages : 599
  • ISBN 10 : 9781107011793
  • Total Read : 83
  • File Size : 15,8 Mb

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Computer Vision Summary:

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

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
  • Total Read : 75
  • File Size : 20,9 Mb

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Mastering Computer Vision with TensorFlow 2 x Summary:

Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key FeaturesGain a fundamental understanding of advanced computer vision and neural network models in use todayCover tasks such as low-level vision, image classification, and object detectionDevelop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkitBook 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 learnExplore methods of feature extraction and image retrieval and visualize different layers of the neural network modelUse TensorFlow for various visual search methods for real-world sc

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
  • Total Read : 70
  • File Size : 20,6 Mb

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Modern Deep Learning and Advanced Computer Vision Summary:

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.

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 : 564
  • ISBN 10 : 978186723xxxx
  • Total Read : 59
  • File Size : 8,7 Mb

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Deep Learning for Computer Vision Summary:

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

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 : 481
  • ISBN 10 : 9781098102333
  • Total Read : 96
  • File Size : 11,8 Mb

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Practical Machine Learning for Computer Vision Summary:

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

Machine Learning in Computer Vision Book

Machine Learning in Computer Vision


  • Author : Nicu Sebe
  • Publisher : Springer Science & Business Media
  • Release Date : 2006-03-30
  • Genre: Computers
  • Pages : 242
  • ISBN 10 : 9781402032752
  • Total Read : 65
  • File Size : 7,6 Mb

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Machine Learning in Computer Vision Summary:

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.