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Advanced Methods and Deep Learning in Computer Vision

Advanced Methods and Deep Learning in Computer Vision
  • Author : E. R. Davies
  • Publsiher : Academic Press
  • Release : 01 October 2021
  • ISBN : 0128221496
  • Pages : 550 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKAdvanced 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

Advanced Methods and Deep Learning in Computer Vision
  • Author : E. R. Davies,Octavia Camps,Matthew Turk
  • Publisher : Academic Press
  • Release : 01 October 2021
GET THIS BOOKAdvanced Methods and Deep Learning in Computer Vision

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

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Advanced Methods and Deep Learning in Computer Vision

Advanced Methods and Deep Learning in Computer Vision
  • Author : E. R. Davies,Octavia Camps,Matthew Turk
  • Publisher : Academic Press
  • Release : 15 October 2021
GET THIS BOOKAdvanced Methods and Deep Learning in Computer Vision

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.

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Deep Learning in Computer Vision

Deep Learning in Computer Vision
  • Author : Mahmoud Hassaballah,Ali Ismail Awad
  • Publisher : CRC Press
  • Release : 23 March 2020
GET THIS BOOKDeep Learning in Computer Vision

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

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Deep Learning in Computer Vision

Deep Learning in Computer Vision
  • Author : Mahmoud Hassaballah,Ali Ismail Awad
  • Publisher : CRC Press
  • Release : 23 March 2020
GET THIS BOOKDeep Learning in Computer Vision

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

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Deep Learning for Vision Systems

Deep Learning for Vision Systems
  • Author : Mohamed Elgendy
  • Publisher : Manning Publications
  • Release : 10 November 2020
GET THIS BOOKDeep Learning for Vision Systems

How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer

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Machine Learning

Machine Learning
  • Author : Hamed Farhadi
  • Publisher : BoD – Books on Demand
  • Release : 19 September 2018
GET THIS BOOKMachine Learning

The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in

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Challenges and Applications for Implementing Machine Learning in Computer Vision

Challenges and Applications for Implementing Machine Learning in Computer Vision
  • Author : Kashyap, Ramgopal,Kumar, A.V. Senthil
  • Publisher : IGI Global
  • Release : 04 October 2019
GET THIS BOOKChallenges and Applications for Implementing Machine Learning in Computer Vision

Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is

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Deep Learning

Deep Learning
  • Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
  • Publisher : MIT Press
  • Release : 10 November 2016
GET THIS BOOKDeep Learning

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.

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Modern Deep Learning and Advanced Computer Vision

Modern Deep Learning and Advanced Computer Vision
  • Author : J. Nedumaan,Prof Thomas Binford,J. Lepika,J. Tisa,J. Ruby,P. S. Jagadeesh Kumar
  • Publisher : Anonim
  • Release : 08 December 2019
GET THIS BOOKModern Deep Learning and Advanced Computer Vision

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

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Mastering Computer Vision with TensorFlow 2.x

Mastering Computer Vision with TensorFlow 2.x
  • Author : Krishnendu Kar
  • Publisher : Packt Publishing Ltd
  • Release : 15 May 2020
GET THIS BOOKMastering Computer Vision with TensorFlow 2.x

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

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Practical Machine Learning and Image Processing

Practical Machine Learning and Image Processing
  • Author : Himanshu Singh
  • Publisher : Apress
  • Release : 26 February 2019
GET THIS BOOKPractical Machine Learning and Image Processing

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

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Machine Learning in Computer Vision

Machine Learning in Computer Vision
  • Author : Nicu Sebe,Ira Cohen,Ashutosh Garg,Thomas S. Huang
  • Publisher : Springer Science & Business Media
  • Release : 30 March 2006
GET THIS BOOKMachine Learning in Computer Vision

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

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Advanced Deep Learning with Keras

Advanced Deep Learning with Keras
  • Author : Rowel Atienza
  • Publisher : Packt Publishing Ltd
  • Release : 31 October 2018
GET THIS BOOKAdvanced Deep Learning with Keras

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

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