Advanced Methods and Deep Learning in Computer Vision

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasising machine and deep learning techniques that have come to the fore during the past 5-10 years. It provides clear explanations of principles and their algorithms supported by applications to demonstrate how vital methods and techniques have been put into practice. Classical topics are presented and taken further by emerging techniques; for example, the impact of learning machines and DNNs in 3D vision and motion. This book provides easy learning for the researcher and practitioner of advanced computer vision methods, and is a suitable textbook for a second course on computer vision, or a deep learning course, for the advanced undergraduate and graduate student. Topics include: achine 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. A reference on deep learning and advanced computer methods by leading people in the field Illustrates the principles with modern, real world applications Suitable self-learning and as a text for graduate courses

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  • Author : E. R. Davies
  • Publisher : Academic Press
  • Pages : 550 pages
  • ISBN : 9780128221099
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKAdvanced Methods and Deep Learning in Computer Vision

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, emphasising machine and deep learning techniques that have come to the fore during the past 5-10 years. It provides clear explanations of principles and their algorithms supported by applications to demonstrate how vital methods and techniques have been put into practice. Classical topics are presented and taken further by emerging techniques; for example, the impact of learning machines and DNNs in 3D vision and motion. This book

Deep Learning in Computer Vision

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  • 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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  • Publisher : Packt Publishing Ltd
  • Release : 23 January 2018
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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

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  • 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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  • Publisher : Packt Publishing Ltd
  • Release : 12 December 2019
GET THIS BOOKAdvanced Deep Learning with Python

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you’ll need to

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  • 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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  • Publisher : IOS Press
  • Release : 01 December 2017
GET THIS BOOKDeep Learning for Image Processing Applications

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

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  • 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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  • Author : Sumit Ranjan,Dr. S. Senthamilarasu
  • Publisher : Packt Publishing Ltd
  • Release : 14 August 2020
GET THIS BOOKApplied Deep Learning and Computer Vision for Self-Driving Cars

This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.

Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling
  • Author : Zhangyang Wang,Yun Fu,Thomas S. Huang
  • Publisher : Academic Press
  • Release : 15 May 2019
GET THIS BOOKDeep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and

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  • Publisher : Packt Publishing Ltd
  • Release : 28 February 2018
GET THIS BOOKDeep Learning By Example

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  • 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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  • Author : Francois Chollet
  • Publisher : Manning Publications
  • Release : 28 October 2017
GET THIS BOOKDeep Learning with Python

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition,

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  • Release : 16 January 2019
GET THIS BOOKPython Deep Learning

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book Description With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current