Practical Applications And Use Cases Of Computer Vision And Recognition Systems
DOWNLOAD
Download Practical Applications And Use Cases Of Computer Vision And Recognition Systems PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Practical Applications And Use Cases Of Computer Vision And Recognition Systems book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Practical Applications And Use Cases Of Computer Vision And Recognition Systems
DOWNLOAD
Author : Chiranji Lal Chowdhary
language : en
Publisher:
Release Date : 2020
Practical Applications And Use Cases Of Computer Vision And Recognition Systems written by Chiranji Lal Chowdhary and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computer vision categories.
"This book contains research on the methods and modern uses of recognition and machine vision technologies in the real world"--
Practical Computer Vision Applications Using Deep Learning With Cnns
DOWNLOAD
Author : Ahmed Fawzy Gad
language : en
Publisher: Apress
Release Date : 2018-12-05
Practical Computer Vision Applications Using Deep Learning With Cnns written by Ahmed Fawzy Gad and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-05 with Computers categories.
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 Python Follow a deep learning project from conception to production using TensorFlow Use NumPy with Kivy to build cross-platform data science applications Who This Book Is ForData scientists, machine learning and deep learning engineers, software developers.
Advancements In Computer Vision Applications In Intelligent Systems And Multimedia Technologies
DOWNLOAD
Author : Sarfraz, Muhammad
language : en
Publisher: IGI Global
Release Date : 2020-05-29
Advancements In Computer Vision Applications In Intelligent Systems And Multimedia Technologies written by Sarfraz, Muhammad and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-29 with Computers categories.
Two significant areas of study that are continually impacting various dimensions in computer science are computer vision and imaging. These technologies are rapidly enhancing how information and data is being exchanged and opening numerous avenues of advancement within areas such as multimedia and intelligent systems. The high level of applicability in computer vision and image processing requires significant research on the specific utilizations of these technologies. Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies is an essential reference source that discusses innovative developments in computational imaging for solving real-life issues and problems and addresses their execution in various disciplines. Featuring research on topics such as image modeling, remote sensing, and support vector machines, this book is ideally designed for IT specialists, scientists, researchers, engineers, developers, practitioners, industry professionals, academicians, and students seeking coverage on the latest developments and innovations in computer vision applications within the realm of multimedia systems.
Analyzing Future Applications Of Ai Sensors And Robotics In Society
DOWNLOAD
Author : Musiolik, Thomas Heinrich
language : en
Publisher: IGI Global
Release Date : 2020-09-11
Analyzing Future Applications Of Ai Sensors And Robotics In Society written by Musiolik, Thomas Heinrich and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-11 with Computers categories.
The rise of artificial intelligence and its countless branches have caused many professional industries to rethink their traditional methods of practice and develop new techniques to keep pace with technological advancement. The continued use of intelligent technologies in the professional world has propelled researchers to contemplate future opportunities and challenges that artificial intelligence may withhold. Significant research is a necessity for understanding future trends of artificial intelligence and the preparation of prospective issues. Analyzing Future Applications of AI, Sensors, and Robotics in Society provides emerging research exploring the potential uses and future challenges of intelligent technological advancements and their impact in education, finance, politics, business, healthcare, and engineering. Featuring coverage on a broad range of topics such as neuronal networks, cognitive computing, and e-health, this book is ideally designed for practitioners, researchers, scientists, executives, strategists, policymakers, academicians, government officials, developers, and students seeking current research on future societal uses of intelligent technology.
Machine Learning In Computer Vision
DOWNLOAD
Author : Nicu Sebe
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-10-04
Machine Learning In Computer Vision written by Nicu Sebe 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 2005-10-04 with Computers categories.
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 selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
Graph Neural Networks Essentials And Use Cases
DOWNLOAD
Author : Pethuru Raj Chelliah
language : en
Publisher: Springer Nature
Release Date : 2025-07-25
Graph Neural Networks Essentials And Use Cases written by Pethuru Raj Chelliah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Computers categories.
This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals—and how they complement advances in generative, agentic, and physical AI—this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data—complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node’s local neighborhood by aggregating information from adjacent nodes, updating the node’s representation in the process. Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance. Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
Investigations In Pattern Recognition And Computer Vision For Industry 4 0
DOWNLOAD
Author : Chowdhary, Chiranji Lal
language : en
Publisher: IGI Global
Release Date : 2023-09-07
Investigations In Pattern Recognition And Computer Vision For Industry 4 0 written by Chowdhary, Chiranji Lal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-07 with Computers categories.
The approaches to computer vision have undergone a long journey in recent years, but still, innovations are continuing with leverage increases in computing power, new data availability, and new ways to leverage machine-learning algorithms. As a branch of artificial intelligence (AI), computer vision brings meaningful information from images and videos. Such innovations help communicators to run better campaigns, amplify messages further, and stand out in a noisy, crowded marketplace. Investigations in Pattern Recognition and Computer Vision for Industry 4.0 provides a holistic discussion of the new practical applications and use cases of computer vision and communications. Covering topics such as social media filters, mobile computer vision, and AI-powered image editing, this book is ideal for academicians, researchers, postgraduate students, professional data analysts, research and development centers, organizations dealing with healthcare informatics, and IT firms.
Deep Learning Applications And Intelligent Decision Making In Engineering
DOWNLOAD
Author : Senthilnathan, Karthikrajan
language : en
Publisher: IGI Global
Release Date : 2020-10-23
Deep Learning Applications And Intelligent Decision Making In Engineering written by Senthilnathan, Karthikrajan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-23 with Technology & Engineering categories.
Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.
Deep Learning For Coders With Fastai And Pytorch
DOWNLOAD
Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Practical Machine Learning And Image Processing
DOWNLOAD
Author : Himanshu Singh
language : en
Publisher: Apress
Release Date : 2019-02-26
Practical Machine Learning And Image Processing written by Himanshu Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-26 with Computers categories.
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 looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the conceptsin Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.