Автор: Hartley, Zisserman Название: Multiple View Geometry in Computer Vision ISBN: 0521540518 ISBN-13(EAN): 9780521540513 Издательство: Cambridge Academ Рейтинг: Цена: 13779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The theory and practice of scene reconstruction are described in detail in a unified framework. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition.
Автор: Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy Название: Deep Learning: Research and Applications ISBN: 3110670798 ISBN-13(EAN): 9783110670790 Издательство: Walter de Gruyter Цена: 20446.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will focus on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it would provide an insight of deep neural networks in action with illustrative coding examples. Moreover, the book will also provide video demonstrations on each chapter. Deep learning is a new area of machine learning research, which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non immediately related fields, for example between air pressure recordings and english words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. The unique features of this book include: • tutorials on deep learning framework with focus on tensor flow, keras etc. • video demonstration of each chapter for enabling the readers to have a good understanding of the chapter contents. • a score of worked out examples on real life applications. • illustrative diagrams • coding examples
Описание: 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.
Автор: Marco, Leo Название: Computer Vision for Assistive Healthcare ISBN: 0128134453 ISBN-13(EAN): 9780128134450 Издательство: Elsevier Science Рейтинг: Цена: 14317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs such as mental functioning, personal mobility, sensory functions and daily living activities. It describes how computer vision techniques - such as image processing, pattern recognition, machine learning and Language Processing and Computer Graphics- can cooperate with Robotics to provide such tools. The range of application areas covered are:
Mental functioning
Personal mobility
Sensory functions
Daily living activities
The reader will learn:
What are the emerging computer vision techniques for supporting mental functioning and to develop a Socially Assistive Robot (SAR)
The algorithms for analysing human behaviour beginning with visual data derived from environmental and wearable sensors ('first person vision')
How smart interfaces and virtual reality tools lead to the development of advanced rehabilitation systems able to perform human action and activity recognition using vision based techniques allowing a natural interaction experience
How Robotics Agent Coachers that can assist people with movement disorders during the execution of motor exercises
The technology behind intelligent wheelchairs
How computer vision technologies have the potential to assist blind people to independently access, understand, and explore the environments (both indoor and outdoor)
Computer vision-based solutions recently employed for safety and health monitoring: Fall detection, life log, and vital parameter monitoring
The first book to give the state-of-the-art computer vision techniques and tools for assistive healthcare
Broad range of topic areas ranging from Image processing, pattern recognition, machine learning to robotics, natural language processing and computer graphics
Wide range of application areas ranging from mobility, sensory substitution, safety and security, to mental and physical rehabilitation and training
Written by leading researchers in this growing field of research
Contains pieces of code
Describes the outstanding research challenges still to be tackled, giving researchers good indicators of research opportunities
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.
For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.
After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.
This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production.
What You Will Learn
Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applications
Who This Book Is For
Data scientists, machine learning and deep learning engineers, software developers.
Описание: This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Автор: Sunila Gollapudi Название: Learn Computer Vision Using OpenCV ISBN: 1484242602 ISBN-13(EAN): 9781484242605 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples. The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python.
The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you'll work with object detection, video storage and interpretation, and human detection using OpenCV.
Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision.
After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work. What You Will LearnUnderstand what computer vision is, and its overall application in intelligent automation systemsDiscover the deep learning techniques required to build computer vision applicationsBuild complex computer vision applications using the latest techniques in OpenCV, Python, and NumPyCreate practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysisWho This Book Is ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.
Автор: Chakraborty, Debarati B Название: Granular video computing: with rough sets, deep learning and in iot ISBN: 981122711X ISBN-13(EAN): 9789811227110 Издательство: World Scientific Publishing Рейтинг: Цена: 12672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.
Автор: Ramo Klevis Название: Hands-On Java Deep Learning for Computer Vision ISBN: 1789613965 ISBN-13(EAN): 9781789613964 Издательство: Неизвестно Рейтинг: Цена: 6068.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will take you through the process of efficiently training deep neural networks in Java for Computer Vision-related tasks. You will build real-world applications ranging from simple Java handwritten digit recognition models to real-time autonomous car driving systems and face recognition models using the popular Java-based libraries.
Описание: Starting from the basics of neural networks, this book covers over 50 applications of computer vision and helps you to gain a solid understanding of the theory of various architectures before implementing them. Each use case is accompanied by a notebook in GitHub with ready-to-execute code and self-assessment questions.
Автор: Venkateswara Hemanth, Panchanathan Sethuraman Название: Domain Adaptation in Computer Vision with Deep Learning ISBN: 3030455289 ISBN-13(EAN): 9783030455286 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Preface.- Part I: Introduction.- Chapter 1: Introduction to Domain Adaptation.- Chapter 2: Shallow Domain Adaptation.- Part II: Domain Alignment in the Feature Space.- Chapter 3: d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding.- Chapter 4: Deep Hashing Network for Unsupervised Domain Adaptation.- Chapter 5: Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation.- Part III: Domain Alignment in the Image Space.- Chapter 6: Unsupervised Domain Adaptation with Duplex Generative Adversarial Network.- Chapter 7: Domain Adaptation via Image to Image Translation.- Chapter 8: Domain Adaptation via Image Style Transfer.- Part IV: Future Directions in Domain Adaptation.- Chapter 9: Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation.- Chapter 10: Adversarial Learning Approach for Open Set Domain Adaptation.- Chapter 11: Universal Domain Adaptation.- Chapter 12: Multi-source Domain Adaptation by Deep CockTail Networks.- Chapter 13: Zero-Shot Task Transfer.
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