Computer Vision Advanced Techniques And Applications
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Computer Vision Advanced Techniques And Applications
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Author : Steve Holden
language : en
Publisher:
Release Date : 2019-06-05
Computer Vision Advanced Techniques And Applications written by Steve Holden and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-05 with categories.
Computer vision is the field of science that is concerned with the development of computers to achieve high-level understanding using digital images or videos. It includes the processes of acquiring, processing and understanding of digital images. It also involves the extraction of data from the real world for the purpose of producing numerical or symbolic information. Some of the areas of interest in computer vision include scene reconstruction, object recognition, 3D pose interpretation, motion estimation, image restoration, etc. The applications of computer vision are in the development of artificial intelligence, surveillance, medical imaging, topographical modeling, navigation, among many others. This book brings forth some of the most innovative concepts and elucidates the unexplored aspects of this discipline. From theories to research to practical applications, studies related to all contemporary topics of relevance to this field have also been included. This book attempts to assist those with a goal of delving into the field of computer vision.
Recent Advances In Computer Vision
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Author : Mahmoud Hassaballah
language : en
Publisher: Springer
Release Date : 2018-12-14
Recent Advances In Computer Vision written by Mahmoud Hassaballah and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-14 with Technology & Engineering categories.
This book presents a collection of high-quality research by leading experts in computer vision and its applications. Each of the 16 chapters can be read independently and discusses the principles of a specific topic, reviews up-to-date techniques, presents outcomes, and highlights the challenges and future directions. As such the book explores the latest trends in fashion creative processes, facial features detection, visual odometry, transfer learning, face recognition, feature description, plankton and scene classification, video face alignment, video searching, and object segmentation. It is intended for postgraduate students, researchers, scholars and developers who are interested in computer vision and connected research disciplines, and is also suitable for senior undergraduate students who are taking advanced courses in related topics. However, it is also provides a valuable reference resource for practitioners from industry who want to keep abreast of recent developments in this dynamic, exciting and profitable research field.
Emerging Topics In Computer Vision And Its Applications
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Author : C. H. Chen
language : en
Publisher: World Scientific
Release Date : 2012
Emerging Topics In Computer Vision And Its Applications written by C. H. Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
This book gives a comprehensive overview of the most advanced theories, methodologies and applications in computer vision. Particularly, it gives an extensive coverage of 3D and robotic vision problems. Example chapters featured are Fourier methods for 3D surface modeling and analysis, use of constraints for calibration-free 3D Euclidean reconstruction, novel photogeometric methods for capturing static and dynamic objects, performance evaluation of robot localization methods in outdoor terrains, integrating 3D vision with force/tactile sensors, tracking via in-floor sensing, self-calibration of camera networks, etc. Some unique applications of computer vision in marine fishery, biomedical issues, driver assistance, are also highlighted.
Mastering Computer Vision With Tensorflow 2 X
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Author : Krishnendu Kar
language : en
Publisher:
Release Date : 2020-05-14
Mastering Computer Vision With Tensorflow 2 X written by Krishnendu Kar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-14 with Computers categories.
You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. You will learn how to use TensorFlow 2.x to build your own neural network model and apply it to various computer vision tasks such as image acquiring, processing, and analyzing.
Challenges And Applications For Implementing Machine Learning In Computer Vision
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Author : Kashyap, Ramgopal
language : en
Publisher: IGI Global
Release Date : 2019-10-04
Challenges And Applications For Implementing Machine Learning In Computer Vision written by Kashyap, Ramgopal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-04 with Computers categories.
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 a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Advanced Topics In Computer Vision
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Author : Giovanni Maria Farinella
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-09-24
Advanced Topics In Computer Vision written by Giovanni Maria Farinella and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-24 with Computers categories.
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 In Computer Vision
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Author : Mahmoud Hassaballah
language : en
Publisher: CRC Press
Release Date : 2020-03-23
Deep Learning In Computer Vision written by Mahmoud Hassaballah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-23 with Computers categories.
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.
Computer Vision
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Author : Information Resources Management Association
language : en
Publisher: Engineering Science Reference
Release Date : 2018
Computer Vision written by Information Resources Management Association and has been published by Engineering Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computer vision categories.
The fields of computer vision and image processing are constantly evolving as new research and applications in these areas emerge. Staying abreast of the most up-to-date developments in this field is necessary in order to promote further research and apply these developments in real-world settings. Computer Vision: Concepts, Methodologies, Tools, and Applications is an innovative reference source for the latest academic material on development of computers for gaining understanding about videos and digital images. Highlighting a range of topics, such as computational models, machine learning, and image processing, this multi-volume book is ideally designed for academicians, technology professionals, students, and researchers interested in uncovering the latest innovations in the field.
Deep Learning For Computer Vision
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Author : Rajalingappaa Shanmugamani
language : en
Publisher: Packt Publishing
Release Date : 2018-01-23
Deep Learning For Computer Vision written by Rajalingappaa Shanmugamani and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-23 with Computers categories.
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 is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python--and some understanding of machine learning concepts--is required to get the best out of this book.
Practical Computer Vision
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Author : Abhinav Dadhich
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-02-05
Practical Computer Vision written by Abhinav Dadhich and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-05 with Computers categories.
A practical guide designed to get you from basics to current state of art in computer vision systems. Key Features Master the different tasks associated with Computer Vision and develop your own Computer Vision applications with ease Leverage the power of Python, Tensorflow, Keras, and OpenCV to perform image processing, object detection, feature detection and more With real-world datasets and fully functional code, this book is your one-stop guide to understanding Computer Vision Book Description In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects. With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications. What you will learn Learn the basics of image manipulation with OpenCV Implement and visualize image filters such as smoothing, dilation, histogram equalization, and more Set up various libraries and platforms, such as OpenCV, Keras, and Tensorflow, in order to start using computer vision, along with appropriate datasets for each chapter, such as MSCOCO, MOT, and Fashion-MNIST Understand image transformation and downsampling with practical implementations. Explore neural networks for computer vision and convolutional neural networks using Keras Understand working on deep-learning-based object detection such as Faster-R-CNN, SSD, and more Explore deep-learning-based object tracking in action Understand Visual SLAM techniques such as ORB-SLAM Who this book is for This book is for machine learning practitioners and deep learning enthusiasts who want to understand and implement various tasks associated with Computer Vision and image processing in the most practical manner possible. Some programming experience would be beneficial while knowing Python would be an added bonus.