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The Hidden Link Between Vision and Learning by Wendy Rosen Pdf
There are inestimable numbers of children who are struggling with learning, and compromised in ways most people are unaware of, because these little-known visual skills are not functioning properly. This can profoundly impact a child's success in school, and in life. The sym...
Optometric Management of Learning-related Vision Problems by Mitchell Scheiman,Michael W. Rouse Pdf
This book explores the relationship between vision and learning and the role of optometrists in the assessment and management of learning related vision problems. It discusses normal child development, the learning process, learning disabilities, the relationship between vision and learning, and models for managing vision problems affecting learning. It is also of interest to health care practitioners involved in the evaluation and treatment of children and adults with learning difficulties. Instructor resources are available; please contact your Elsevier sales representative for details. Presents an organized, easy-to-follow approach to the diagnosis and treatment of learning-related vision problems.Each chapter contains key terms and chapter review questions making it more appealing to the student and instructor.Includes appendices containing sample reports, sample questionnaires, sample letters, a bibliography, and case histories showing the reader how to use the material from the book in practice.Well respected authors and contributors provide authoritative coverage of the topic. Expanded information on the use of colored lenses and reading.New chapter on reading disorders that covers how children learn to read, teaching methods, optometric assessment, and management of dyslexia.Chapters have been updated with new computer software options, including computer aided vision therapy, perceptual home therapy system, and temporal visual processing program.Updated testing battery, including new tests, visual processing speed, and optometric use of IQ screening tests such as K-BIT.Expanded coverage of psycho education evaluation includes substantial updates with new test instruments, such as WISC.Substantial revisions based on literature review for last 10 years.New and updated illustrations.
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
Author : Simon J. D. Prince Publisher : Cambridge University Press Page : 599 pages File Size : 49,5 Mb Release : 2012-06-18 Category : Computers ISBN : 9781107011793
Practical Machine Learning for Computer Vision by Valliappa Lakshmanan,Martin Görner,Ryan Gillard Pdf
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Learning to See = Seeing to Learn by Patrick Quaid,Stephanie Beaudette,Daniel Cunningham Pdf
Learning to See = Seeing to Learn: Vision, Learning & Behavior by Dr. Patrick Quaid et al, will unlock the connection between eyesight, education, and behaviors in children. How can you have 20/20 vision and not see well? Why are some very bright children unable to concentrate while reading and are unable to memorize what they see? This book will be especially interesting for teachers, educators, and parents with children who struggle to learn, are on an IEP, or who have been identified with ADD or ADHD. Learn about visual processing and why some children and adults struggle to see and understand what everyone else does naturally. Discover how challenges with visual processing can be corrected.
Poor vision, if unnoticed and untreated, can dramatically reduce a childOs school achievement. While it is easy to blame underachievement on a variety of causes, the real cause may be directly related to vision development. Strong vision is much more than being able to see the blackboard from the back row. Solid visual skills that underlie brain learning are developed through a variety of activities that are less and less a part of childrenOs lives thanks to TV and video games. Eyes for Learning explains how parents and teachers can spot a vision-related learning problem and how to treat it. Dr. Antonia Orfield provides answers about referrals, required vision tests, and vision-improvement techniques. The bottom line is that good vision is a learned skill that is best developed by the practices explained in this book. Understanding these explanations can go a long way in saving a child from failure in school.
Robotic Vision: Technologies for Machine Learning and Vision Applications by Garcia-Rodriguez, Jose Pdf
Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.
Deep Learning in Computer Vision by Mahmoud Hassaballah,Ali Ismail Awad Pdf
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.
Explainable and Interpretable Models in Computer Vision and Machine Learning by Hugo Jair Escalante,Sergio Escalera,Isabelle Guyon,Xavier Baró,Yağmur Güçlütürk,Umut Güçlü,Marcel van Gerven Pdf
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
Envisioning the Future of Learning for Creativity, Innovation and Entrepreneurship by Kyriaki Papageorgiou,Olga Kokshagina Pdf
Envisioning the Future of Learning for Creativity, Innovation and Entrepreneurship outlines the work and findings of the Erasmus+ VISION research project. Education is changing and teachers and students around the world are reshaping it. This book is designed to help educators, policy makers and stakeholders from industry and society at large navigate the changing landscape of education for creativity, innovation, and entrepreneurship (CIE). Built on insights from more than 250 experts, the book presents a learning landscape that captures today’s shifts within CIE education and proposes guidance and potential pathways for those involved in the field. The book shows that the landscape of education for CIE is influenced by: Learning as an immersive experience driven by play and experimentation The rise in on the job education and learning by doing as part of life-long upskilling Teachers’ roles evolve to be coaches and mentors developing hard and soft skills Numerous images are included in the book using the technique of visual thinking, stimulating imagination, creativity and innovation.
Leading Modern Learning by Jay McTighe,Yong Zhao Pdf
In the second edition of Leading Modern Learning, A Blueprint for Vision Driven Schools authors Jay McTighe and Greg Curtis offer the reader a fully rethought version of their blueprint for major education reform. More than a simple refresh, this new edition incorporates new insights, thinking, and experiences to refine approaches to, and tools for, implementing effective modern learning practices in a department, school, or district. With new Notes From the Field elements, McTighe and Curtis highlight key observations from their work with schools, including how to avoid potential missteps, misunderstandings, and time wasters that inhibit progress when implementing reform. .
A revelatory account of the brain's capacity for change When neuroscientist Susan Barry was fifty years old, she experienced the sense of immersion in a three dimensional world for the first time. Skyscrapers on street corners appeared to loom out toward her like the bows of giant ships. Tree branches projected upward and outward, enclosing and commanding palpable volumes of space. Leaves created intricate mosaics in 3D. Barry had been cross-eyed and stereoblind since early infancy. After half a century of perceiving her surroundings as flat and compressed, on that day she saw the city of Manhattan in stereo depth for first time in her life. As a neuroscientist, she understood just how extraordinary this transformation was, not only for herself but for the scientific understanding of the human brain. Scientists have long believed that the brain is malleable only during a "critical period" in early childhood. According to this theory, Barry's brain had organized itself when she was a baby to avoid double vision - and there was no way to rewire it as an adult. But Barry found an optometrist who prescribed a little-known program of vision therapy; after intensive training, Barry was ultimately able to accomplish what other scientists and even she herself had once considered impossible. Dubbed "Stereo Sue" by renowned neurologist Oliver Sacks, Susan Barry tells her own remarkable journey and celebrates the joyous pleasure of our senses.
Challenges and Applications for Implementing Machine Learning in Computer Vision by Kashyap, Ramgopal,Kumar, A.V. Senthil Pdf
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.
