Deep Learning In Visual Computing

Deep Learning In Visual Computing Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Deep Learning In Visual Computing book. This book definitely worth reading, it is an incredibly well-written.

Deep Learning in Visual Computing

Author : Hassan Ugail
Publisher : CRC Press
Page : 144 pages
File Size : 46,8 Mb
Release : 2022-07-07
Category : Computers
ISBN : 9781000625455

Get Book

Deep Learning in Visual Computing by Hassan Ugail Pdf

Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing. This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.

Deep Learning in Visual Computing and Signal Processing

Author : Krishna Kant Singh,Vibhav Kumar Sachan,Akansha Singh,Sanjeevikumar Padmanaban
Publisher : CRC Press
Page : 289 pages
File Size : 49,5 Mb
Release : 2022-10-20
Category : Science
ISBN : 9781000565232

Get Book

Deep Learning in Visual Computing and Signal Processing by Krishna Kant Singh,Vibhav Kumar Sachan,Akansha Singh,Sanjeevikumar Padmanaban Pdf

Covers both the fundamentals and the latest concepts in deep learning Presents some of the diverse applications of deep learning in visual computing and signal processing Includes over 90 figures and tables to elucidate the text

Convolutional Neural Networks in Visual Computing

Author : Ragav Venkatesan,Baoxin Li
Publisher : CRC Press
Page : 187 pages
File Size : 44,6 Mb
Release : 2017-10-23
Category : Computers
ISBN : 9781498770408

Get Book

Convolutional Neural Networks in Visual Computing by Ragav Venkatesan,Baoxin Li Pdf

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

Deep Learning in Mining of Visual Content

Author : Akka Zemmari,Jenny Benois-Pineau
Publisher : Springer Nature
Page : 117 pages
File Size : 48,6 Mb
Release : 2020-01-22
Category : Computers
ISBN : 9783030343767

Get Book

Deep Learning in Mining of Visual Content by Akka Zemmari,Jenny Benois-Pineau Pdf

This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The authors give a fresh view on Deep learning approaches both from the point of view of image understanding and supervised machine learning. It contains chapters which introduce theoretical and mathematical foundations of neural networks and related optimization methods. Then it discusses some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. Deep Learning is currently at the heart of most cutting edge technologies. It is in the core of the recent advances in Artificial Intelligence. Visual information in Digital form is constantly growing in volume. In such active domains as Computer Vision and Robotics visual information understanding is based on the use of deep learning. Other chapters present applications of deep learning for visual content mining. These include attention mechanisms in deep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how deep learning can be of very high interest to computer-aided diagnostics of Alzheimer’s disease on multimodal imaging. This book targets advanced-level students studying computer science including computer vision, data analytics and multimedia. Researchers and professionals working in computer science, signal and image processing may also be interested in this book.

Advanced Methods and Deep Learning in Computer Vision

Author : E. R. Davies,Matthew Turk
Publisher : Academic Press
Page : 584 pages
File Size : 51,7 Mb
Release : 2021-11-09
Category : Computers
ISBN : 9780128221495

Get Book

Advanced Methods and Deep Learning in Computer Vision by E. R. Davies,Matthew Turk Pdf

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

Deep Learning

Author : Andrew Glassner
Publisher : No Starch Press
Page : 1239 pages
File Size : 43,7 Mb
Release : 2021-06-22
Category : Computers
ISBN : 9781718500730

Get Book

Deep Learning by Andrew Glassner Pdf

A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: • How text generators create novel stories and articles • How deep learning systems learn to play and win at human games • How image classification systems identify objects or people in a photo • How to think about probabilities in a way that's useful to everyday life • How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations

Advances in Visual Computing

Author : George Bebis,Richard Boyle,Bahram Parvin,Darko Koracin,Matt Turek,Srikumar Ramalingam,Kai Xu,Stephen Lin,Bilal Alsallakh,Jing Yang,Eduardo Cuervo,Jonathan Ventura
Publisher : Springer
Page : 771 pages
File Size : 50,7 Mb
Release : 2018-11-09
Category : Computers
ISBN : 9783030038014

Get Book

Advances in Visual Computing by George Bebis,Richard Boyle,Bahram Parvin,Darko Koracin,Matt Turek,Srikumar Ramalingam,Kai Xu,Stephen Lin,Bilal Alsallakh,Jing Yang,Eduardo Cuervo,Jonathan Ventura Pdf

This book constitutes the refereed proceedings of the 13th International Symposium on Visual Computing, ISVC 2018, held in Las Vegas, NV, USA in November 2018. The total of 66 papers presented in this volume was carefully reviewed and selected from 91 submissions. The papers are organized in topical sections named: ST: computational bioimaging; computer graphics; visual surveillance; pattern recognition; vitrual reality; deep learning; motion and tracking; visualization; object detection and recognition; applications; segmentation; and ST: intelligent transportation systems.

Advances in Visual Computing

Author : George Bebis,Richard Boyle,Bahram Parvin,Darko Koracin,Daniela Ushizima,Sek Chai,Shinjiro Sueda,Xin Lin,Aidong Lu,Daniel Thalmann,Chaoli Wang,Panpan Xu
Publisher : Springer Nature
Page : 718 pages
File Size : 50,9 Mb
Release : 2019-10-25
Category : Computers
ISBN : 9783030337209

Get Book

Advances in Visual Computing by George Bebis,Richard Boyle,Bahram Parvin,Darko Koracin,Daniela Ushizima,Sek Chai,Shinjiro Sueda,Xin Lin,Aidong Lu,Daniel Thalmann,Chaoli Wang,Panpan Xu Pdf

This book constitutes the refereed proceedings of the 14th International Symposium on Visual Computing, ISVC 2019, held in Lake Tahoe, NV, USA in October 2019. The 100 papers presented in this double volume were carefully reviewed and selected from 163 submissions. The papers are organized into the following topical sections: Deep Learning I; Computer Graphics I; Segmentation/Recognition; Video Analysis and Event Recognition; Visualization; ST: Computational Vision, AI and Mathematical methods for Biomedical and Biological Image Analysis; Biometrics; Virtual Reality I; Applications I; ST: Vision for Remote Sensing and Infrastructure Inspection; Computer Graphics II; Applications II; Deep Learning II; Virtual Reality II; Object Recognition/Detection/Categorization; and Poster.

Deep Learning to See

Author : Alessandro Betti,Marco Gori,Stefano Melacci
Publisher : Springer Nature
Page : 116 pages
File Size : 46,6 Mb
Release : 2022-04-26
Category : Computers
ISBN : 9783030909871

Get Book

Deep Learning to See by Alessandro Betti,Marco Gori,Stefano Melacci Pdf

The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this work criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature. This work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis proposed is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal. Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. As such, it will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.

Visual Object Tracking with Deep Neural Networks

Author : Pier Luigi Mazzeo,Srinivasan Ramakrishnan,Paolo Spagnolo
Publisher : BoD – Books on Demand
Page : 208 pages
File Size : 40,7 Mb
Release : 2019-12-18
Category : Computers
ISBN : 9781789851571

Get Book

Visual Object Tracking with Deep Neural Networks by Pier Luigi Mazzeo,Srinivasan Ramakrishnan,Paolo Spagnolo Pdf

Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.

Advances in Visual Computing

Author : George Bebis,Bo Li,Angela Yao,Yang Liu,Ye Duan,Manfred Lau,Rajiv Khadka,Ana Crisan,Remco Chang
Publisher : Springer Nature
Page : 486 pages
File Size : 43,6 Mb
Release : 2022-12-10
Category : Computers
ISBN : 9783031207136

Get Book

Advances in Visual Computing by George Bebis,Bo Li,Angela Yao,Yang Liu,Ye Duan,Manfred Lau,Rajiv Khadka,Ana Crisan,Remco Chang Pdf

This two-volume set of LNCS 13598 and 13599 constitutes the refereed proceedings of the 17th International Symposium on Visual Computing, ISVC 2022, which was held in October 2022. The 61 papers presented in these volumes were carefully reviewed and selected from 110 submissions. They are organized in the following topical sections: Part I: ​deep learning I; visualization; object detection and recognition; deep learning II; video analysis and event recognition; computer graphics; ST: biomedical imaging techniques for cancer detection, diagnosis and management. Part II: ​ST: neuro-inspired artificia intelligence; applications; segmentation and tracking; virtual reality; poster.

Image Processing and Computer Vision in iOS

Author : Oge Marques
Publisher : Springer Nature
Page : 66 pages
File Size : 46,8 Mb
Release : 2020-11-23
Category : Computers
ISBN : 9783030540326

Get Book

Image Processing and Computer Vision in iOS by Oge Marques Pdf

This book presents the fundamentals of mobile visual computing in iOS development and provides directions for developers and researchers interested in developing iOS applications with image processing and computer vision capabilities. Presenting a technical overview of some of the tools, languages, libraries, frameworks, and APIs currently available for developing iOS applications Image Processing and Computer Vision in iOS reveals the rich capabilities in image processing and computer vision. Its main goal is to provide a road map to what is currently available, and a path to successfully tackle this rather complex but highly rewarding task.

Deep Learning for Vision Systems

Author : Mohamed Elgendy
Publisher : Manning Publications
Page : 478 pages
File Size : 40,6 Mb
Release : 2020-11-10
Category : Computers
ISBN : 9781617296192

Get Book

Deep Learning for Vision Systems by Mohamed Elgendy Pdf

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 vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book 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. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings

Advances in Visual Computing

Author : George Bebis,Golnaz Ghiasi,Yi Fang,Andrei Sharf,Yue Dong,Chris Weaver,Zhicheng Leo,Joseph J. LaViola Jr.,Luv Kohli
Publisher : Springer Nature
Page : 506 pages
File Size : 44,7 Mb
Release : 2024-01-03
Category : Computers
ISBN : 9783031479663

Get Book

Advances in Visual Computing by George Bebis,Golnaz Ghiasi,Yi Fang,Andrei Sharf,Yue Dong,Chris Weaver,Zhicheng Leo,Joseph J. LaViola Jr.,Luv Kohli Pdf

This volume LNCS 14361 and 14362 constitutes the refereed proceedings of the, 16th International Symposium, ISVC 2023, in October 2023, held at Lake Tahoe, NV, USA. The 42 full papers and 13 poster papers were carefully reviewed and selected from 120 submissions. A total of 25 papers were also accepted for oral presentation in special tracks from 34 submissions. The following topical sections followed as: Part 1: ST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management; Visualization; Video Analysis and Event Recognition; ST: Innovations in Computer Vision & Machine Learning for Critical & Civil Infrastructures; ST: Generalization in Visual Machine Learning; Computer Graphics; Medical Image Analysis; Biometrics; Autonomous Anomaly Detection in Images; ST: Artificial Intelligence in Aerial and Orbital Imagery; ST: Data Gathering, Curation, and Generation for Computer Vision and Robotics in Precision Agriculture. Part 2: Virtual Reality; Segmentation; Applications; Object Detection and Recognition; Deep Learning; Poster.

Advances in Visual Computing

Author : George Bebis,Golnaz Ghiasi,Yi Fang,Andrei Sharf,Yue Dong,Chris Weaver,Zhicheng Leo,Joseph J. LaViola Jr.,Luv Kohli
Publisher : Springer Nature
Page : 630 pages
File Size : 48,9 Mb
Release : 2023-11-30
Category : Computers
ISBN : 9783031479694

Get Book

Advances in Visual Computing by George Bebis,Golnaz Ghiasi,Yi Fang,Andrei Sharf,Yue Dong,Chris Weaver,Zhicheng Leo,Joseph J. LaViola Jr.,Luv Kohli Pdf

This volume LNCS 14361 and 14362 constitutes the refereed proceedings of the, 16th International Symposium, ISVC 2023, in October 2023, held at Lake Tahoe, NV, USA. The 42 full papers and 13 poster papers were carefully reviewed and selected from 120 submissions. A total of 25 papers were also accepted for oral presentation in special tracks from 34 submissions. The following topical sections followed as: Part 1: ST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management; Visualization; Video Analysis and Event Recognition; ST: Innovations in Computer Vision & Machine Learning for Critical & Civil Infrastructures; ST: Generalization in Visual Machine Learning; Computer Graphics; Medical Image Analysis; Biometrics; Autonomous Anomaly Detection in Images; ST: Artificial Intelligence in Aerial and Orbital Imagery; ST: Data Gathering, Curation, and Generation for Computer Vision and Robotics in Precision Agriculture. Part 2: Virtual Reality; Segmentation; Applications; Object Detection and Recognition; Deep Learning; Poster.