This tutorial explains the human eye, its function, and performance limits from the perspective of an experienced optical engineer and lens designer. It is concise and readable, with examples and data, and is intended for students, practicing engineers, and technology users.

Building on the successful formula of the first edition, Martin Tovée offers a concise but detailed account of how the visual system is organised and functions to produce visual perception. He takes his readers from first principles; the structure and function of the eye and what happens when light enters, to how we see and process images, recognise patterns and faces, and through to the most recent discoveries in molecular genetics and brain imaging, and how they have uncovered a host of new advances in our understanding of how visual information is processed within the brain. Incorporating new material throughout, including almost 50 new images, every chapter has been updated to include the latest research, and culminates in helpful key points, which summarise the lessons learnt. This book is an invaluable course text for students within the fields of psychology, neuroscience, biology and physiology.

Some of the best vision scientists in the world in their respective fields have contributed to chapters in this book. They have expertise in a wide variety of fields, including bioengineering, basic and clinical visual science, medicine, neurophysiology, optometry, and psychology. Their combined efforts have resulted in a high quality book that covers modeling and quantitative analysis of optical, neurosensory, oculomotor, perceptual and clinical systems. It includes only those techniques and models that have such fundamentally strong physiological, control system, and perceptual bases that they will serve as foundations for models and analysis techniques in the future. The book is aimed first towards seniors and beginning graduate students in biomedical engineering, neurophysiology, optometry, and psychology, who will gain a broad understanding of quantitative analysis of the visual system. In addition, it has sufficient depth in each area to be useful as an updated reference and tutorial for graduate and post-doctoral students, as well as general vision scientists.

These are the proven benefits of implementing "visual systems" - a highly successful lean-production approach that uses visual indicators, signals, controls, and guarantees to direct and support activities on the shop floor. The result is a self-explaining and self-regulating workplace where critical information is shared rapidly, accurately, and without speaking a word. Visual Systems is a comprehensive look at how to implement this breakthrough approach. Any company can use Dr. Gwendolyn D. Galsworth's approach to organize, share, and visually manage the thousands of location details on which the daily life of an enterprise depends. Use this book to build common sense and a common improvement language directly into the workplace and put an end to costly secrets, surprises, and microsupervision.

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

To respond quickly to a changing marketplace with flexible production goals and zero defects, you need to understand at a glance what is going on in your workplace. Visual Control Systems will help everyone in your workplace become involved in monitoring the manufacturing process in a variety of ways. You'll learn how to plan and promote a visual control system factory-wide, how to implement the system thoroughly, and how to integrate it with a 5S (industrial housekeeping) program and continuous improvement. It Includes: An introductory overview of visual control systems and their applications to factory management. Explains the basics of visual control systems; identifying the manufacturing areas where visual controls are most important. Case studies on the 5S approach using visual control systems. Visual information is the key to focusing all the players on your manufacturing team on their common objectives. This valuable sourcebook is full of ideas you can use so the teammates know the score, all the time.

A question often asked of those of us who work in the seemingly esoteric field of fish vision is, why? To some of us the answer seems obvious - how many other visual scientists get to dive in a tropical lagoon in the name of science and then are able to eat their subjects for dinner? However, there are better, or at least scientifically more acceptable, reasons for working on the visual system of fish. First, in terms of numbers, fish are by far the most important of all vertebrate classes, probably accounting for over half (c. 22 000 species) of all recognized vertebrate species (Nelson, 1984). Furthermore, many of these are of commercial importance. Secondly, if one of the research aims is to understand the human visual system, animals such as fish can tell us a great deal, since in many ways their visual systems, and specifically their eyes, are similar to our own. This is fortunate, since there are several techniques, such as intracellular retinal recording, which are vital to our understanding of the visual process, that cannot be performed routinely on primates. The cold blooded fish, on the other hand, is an ideal subject for such studies and much of what we know about, for example, the fundamentals of information processing in the retina is based on work carried out on fish (e. g. Svaetichin, 1953).

Extra Bold is the inclusive, practical, and informative (design) career guide for everyone! Part textbook and part comic book, zine, manifesto, survival guide, and self-help manual, Extra Bold is filled with stories and ideas that don't show up in other career books or design overviews. • Both pragmatic and inquisitive, the book explores power structures in the workplace and how to navigate them. • Interviews showcase people at different stages of their careers. • Biographical sketches explore individuals marginalized by sexism, racism, and ableism. • Practical guides cover everything from starting out, to wage gaps, coming out at work, cover letters, mentoring, and more. A new take on the design canon. • Opens with critical essays that rethink design principles and practices through theories of feminism, anti-racism, inclusion, and nonbinary thinking. • Features interviews, essays, typefaces, and projects from dozens of contributors with a variety of racial and ethnic backgrounds, abilities, gender identities, and positions of economic and social privilege. • Adds new voices to the dominant design canon. Written collaboratively by a diverse team of authors, with original, handcrafted illustrations by Jennifer Tobias that bring warmth, happiness, humor, and narrative depth to the book. Extra Bold is written by Ellen Lupton (Thinking with Type), Farah Kafei, Jennifer Tobias, Josh A. Halstead, Kaleena Sales, Leslie Xia, and Valentina Vergara.

This interdisciplinary work brings you to the cutting edge of emerging technologies inspired by human sight, ranging from semiconductor photoreceptors based on novel organic polymers and retinomorphic processing circuitry to low-powered devices that replicate spatial and temporal processing in the brain. Moreover, it is the first work of its kind that integrates the full range of physiological, engineering, and mathematical issues and advances together in a single source.

This book is the only account of what honeybees actually see. Bees detect some visual features such as edges and colours, but there is no sign that they reconstruct patterns or put together features to form objects. Bees detect motion but have no perception of what it is that moves, and certainly they do not recognize "things" by their shapes. Yet they clearly see well enough to fly and find food with a minute brain. Bee vision is therefore relevant to the construction of simple artificial visual systems, for example for mobile robots. The surprising conclusion is that bee vision is adapted to the recognition of places, not things. In this volume, Adrian Horridge also sets out the curious and contentious history of how bee vision came to be understood, with an account of a century of neglect of old experimental results, errors of interpretation, sharp disagreements, and failures of the scientific method. The design of the experiments and the methods of making inferences from observations are also critically examined, with the conclusion that scientists are often hesitant, imperfect and misleading, ignore the work of others, and fail to consider alternative explanations. The erratic path to understanding makes interesting reading for anyone with an interest in the workings of science but particularly those researching insect vision and invertebrate sensory systems.