Human And Machine Vision

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

Spans the fields of perceptual psychology & computer vision. Offers a discussion of visual problems of common interest to the fields of human visual perception & machine vision. Includes representation, segmentation, organization, motion & space.

The following are the proceedings of the Third International Workshop on Perception held in Pavia, Italy, on September 27-30, 1993, under the auspices of four institutions: the Group of Cybernetic and Biophysics (GNCB)s of the National Research Council (CNR), the Italian Association for Artificial Intelligence (AI * IA), the Italian Association of Psychology (AlP), and the Italian Chapter of the International Association for Pattern Recognition (IAPR). The theme of this third workshop was: "Human and Machine Vision: Analogies and Divergencies." A wide spectrum of topics was covered, ranging from neurophysiology, to computer architecture, to psychology, to image understanding, etc. For this reason the structure of this workshop was quite different from those of the first two held in Parma (1991), and Trieste (1992). This time the workshop was composed of just eight modules, each one consisting of two invited lectures (dealing with vision in nature and machines, respectively) and a common panel discussion (including the two lecturers and three invited panellists).

Perspectives in Computing: Human and Machine Vision II compiles papers presented at the second Workshop on Human and Machine Vision held in Montreal, Canada on August 1-3, 1984. This book discusses the perception of transparency in man and machine, human image understanding, and connectionist models and parallelism in high level vision. The theory of the perceived spatial layout of scenes, generative systems of analyzers, and codon constraints on closed 2D shapes are also elaborated. This text likewise covers the environment- and viewer-centered perception of surface orientation, autonomous scene description with range imagery, and pre-attentive processing in vision. This publication is recommended for students and researchers interested in both fields of visual perception and computer vision.

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.

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans.

This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Features: with a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includes an extensive glossary; discusses the problems associated with detecting and tracking people through camera networks; examines topics related to determining the time-varying 3D pose of a person from video; investigates the representation and recognition of human and vehicular actions; reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance.

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans.