Human and Machine Vision

by Jacob Beck

Author	: Jacob Beck
Publisher	: Academic Press
Total Pages	: 580
Release	: 2014-06-20
Genre	: Reference
ISBN	: 1483266966

GET BOOK

Human and Machine Vision provides information pertinent to an interdisciplinary program of research in visual perception. This book presents a psychophysical study of the human visual system, which provides insights on how to model the flexibility required by a general-purpose visual system. Organized into 17 chapters, this book begins with an overview of how a visual display is segmented into components on the basis of textual differences. This text then proposes three criteria for judging representations of shape. Other chapters consider an increased use of machine vision programs as models of human vision and of data from human vision in developing programs for machine vision. This book discusses as well the diversity and flexibility of systems for representing visual information. The final chapter deals with dot patterns and discusses the process of interring orientation information from collections of them. This book is a valuable resource for psychologists, neurophysiologists, and computer scientists.

Computer Vision for Human-Machine Interaction

by Roberto Cipolla

Author	: Roberto Cipolla
Publisher	: Cambridge University Press
Total Pages	: 364
Release	: 1998-07-13
Genre	: Computers
ISBN	: 9780521622530

GET BOOK

Leading scientists describe how advances in computer vision can change how we interact with computers.

Human-in-the-Loop Machine Learning

by Robert Munro

Author	: Robert Munro
Publisher	: Simon and Schuster
Total Pages	: 422
Release	: 2021-07-20
Genre	: Computers
ISBN	: 1617296740

GET BOOK

Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.

Human Recognition in Unconstrained Environments

by Maria De Marsico

Author	: Maria De Marsico
Publisher	: Academic Press
Total Pages	: 250
Release	: 2017-01-09
Genre	: Computers
ISBN	: 0081007124

GET BOOK

Human Recognition in Unconstrained Environments provides a unique picture of the complete ‘in-the-wild’ biometric recognition processing chain; from data acquisition through to detection, segmentation, encoding, and matching reactions against security incidents. Coverage includes: Data hardware architecture fundamentals Background subtraction of humans in outdoor scenes Camera synchronization Biometric traits: Real-time detection and data segmentation Biometric traits: Feature encoding / matching Fusion at different levels Reaction against security incidents Ethical issues in non-cooperative biometric recognition in public spaces With this book readers will learn how to: Use computer vision, pattern recognition and machine learning methods for biometric recognition in real-world, real-time settings, especially those related to forensics and security Choose the most suited biometric traits and recognition methods for uncontrolled settings Evaluate the performance of a biometric system on real world data Presents a complete picture of the biometric recognition processing chain, ranging from data acquisition to the reaction procedures against security incidents Provides specific requirements and issues behind each typical phase of the development of a robust biometric recognition system Includes a contextualization of the ethical/privacy issues behind the development of a covert recognition system which can be used for forensics and security activities

The Alignment Problem: Machine Learning and Human Values

by Brian Christian

Author	: Brian Christian
Publisher	: W. W. Norton & Company
Total Pages	: 459
Release	: 2020-10-06
Genre	: Science
ISBN	: 039363583X

GET BOOK

A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.

Human + Machine

by Paul R. Daugherty

Author	: Paul R. Daugherty
Publisher	: Harvard Business Press
Total Pages	: 264
Release	: 2018-03-20
Genre	: Computers
ISBN	: 1633693872

GET BOOK

AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader’s guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.

Practical Machine Learning for Computer Vision

by Valliappa Lakshmanan

Author	: Valliappa Lakshmanan
Publisher	: "O'Reilly Media, Inc."
Total Pages	: 481
Release	: 2021-07-21
Genre	: Computers
ISBN	: 1098102339

GET BOOK

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

Biological and Computer Vision

by Gabriel Kreiman

Author	: Gabriel Kreiman
Publisher	: Cambridge University Press
Total Pages	: 275
Release	: 2021-02-04
Genre	: Computers
ISBN	: 1108483437

GET BOOK

This book introduces neural mechanisms of biological vision and how artificial intelligence algorithms learn to interpret images.

Digital Images and Human Vision

by Andrew B. Watson

Author	: Andrew B. Watson
Publisher	: Bradford Books
Total Pages	: 224
Release	: 1993
Genre	: Computers
ISBN	: 9780262231718

GET BOOK

These fifteen contributions by distinguished vision and imaging scientists explore the role of human vision in the design of modem image communication systems. A dominant theme in the book is image compression—how compression algorithms can be designed to make best use of what we know about human vision. Electronic image communications, which encompass television, high-definition television, teleconferencing, multimedia, digital photography, desktop publishing, and digital movies, is a rapidly growing segment of technology and business. Because these products and technologies are designed for human viewing, knowledge of human perception is essential to optimal design. This book provides a timely compendium of important ideas and perspectives on such subjects as the key aspects of human visual sensitivity that are relevant to image communications and, conversely, the major problems in image communications that vision science can address; the mathematical models of human vision that are useful in the design of image comunications systems; reliable and efficient methods of evaluating visual quality; and aspects of human vision that can be exploited to provide substantial improvements in coding efficiency. Andrew B. Watson is Senior Scientist for Vision Research at NASA. Contributors: Albert J. Ahumada, Jr. E. Barth. V. Michael Bove, Jr. Gershon Buchsbaum. Phillipe Cassereau. Pamela C. Cosman. Scott J. Daly. Michael Eckert. Bernd Girod. William E. Glenn. Robert M. Gray. Paul J. Hearty. Bradley Horowitz. Stanley Klein. Jeffrey Lubin, Cynthia Null. Karen L. Oehler. Alex Pentland. Todd Reed. Andrew B. Watson. B. Wegmann. Christof Zetsche.