Making a Machine That Sees Like Us explains why and how our visual perceptions can provide us with an accurate representation of the external world. Along the way, it tells the story of a machine (a computational model) built by the authors that solves the computationally difficult problem of seeing the way humans do. This accomplishment required a radical paradigm shift - one that challenged preconceptions about visual perception and tested the limits of human behavior-modeling for practical application.
The text balances scientific sophistication and compelling storytelling, making it accessible to both technical and general readers. Online demonstrations and references to the authors' previously published papers detail how the machine was developed and what drove the ideas needed to make it work. The authors contextualize their new theory of shape perception by highlighting criticisms and opposing theories, offering readers a fascinating account not only of their revolutionary results, but of the scientific process that guided the way.
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About the Author:
"This book is timely, interesting, even provocative, and well worth reading. It is especially likely to be of considerable interest to the mathematical psychology community because vision has always enjoyed a special status in the field." --Journal of Mathematical Psychology
Zygmunt Pizlo is a professor of Psychological Sciences and of Electrical and Computer Engineering at Purdue University. He has published over 100 journal and conference papers on all aspects of vision as well as on problem-solving. In 2008, he published the first book devoted to 3D shape-perception.
Yunfeng Li is a postdoctoral fellow at Purdue University. His research interests focus on applying psychophysics and mathematics to explore and model human visual perception of 3D shapes and scenes, regularization and Bayesian methods, and human and robot visual navigation.
Tadamasa Sawada is a postdoctoral researcher in the Graduate Center for Vision Research at SUNY College of Optometry. He has received his Ph.D. from the Tokyo Institute of Technology in 2006 and had worked as a postdoctoral researcher at Purdue University (2006-2013) and at the Ohio State University (2013-2014). He has been studying human visual perception using psychophysical experiments as well as mathematical and computational modeling.
Robert M. Steinman devoted most of his scientific career, which began in 1964, to sensory and perceptual process, heading this specialty area in the Department of Psychology at the University of Maryland in College Park until his retirement in 2008. Most of his publications, before collaborating on shape perception with Prof. Pizlo, were concerned with human eye movements. Prof. Steinman, with Prof. Azriel Rosenfeld of the Center for Automation Research at UMD, supervised Prof. Pizlo's doctoral degree in Psychology, which was awarded in 1991. Prof. Steinman has been collaborating with Prof. Pizlo in his studies of shape perception since 2000.
"Written in a conversational manner, the book outlines in a step-by-step fashion the rationale for the model and some important ways in which the model differs from other contemporary
computational approaches. Making a Machine That Sees Like Us is an important book for anyone with an interest in machine vision for it offers a bottom-up approach to object perception that incorporates a priori constraints rather than sensory data alone. No doubt the inclusion of sensory data together with the constraints is the determining factor in its success as a model of machine vision. It is written in a style that is easy to read by those who do not have much background in visual perception." --PsycCRITIQUES
"This book is timely, interesting, even provocative, and well worth reading. It is especially likely to be of considerable interest to the mathematical psychology community because vision has always enjoyed a special status in the field." --Journal of Mathematical Psychology
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