Zygmunt Pizlo – författare

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.

Intelligent mental representations of physical, cognitive and social environments allow humans to navigate enormous search spaces, whose sizes vastly exceed the number of neurons in the human brain. This allows us to solve a wide range of problems, such as the Traveling Salesperson Problem, insight problems, as well as mathematics and physics problems. As an area of research, problem solving has steadily grown over time. Researchers in Artificial Intelligence have been formulating theories of problem solving for the last 70 years. Psychologists, on the other hand, have focused their efforts on documenting the observed behavior of subjects solving problems. This book represents the first effort to merge the behavioral results of human subjects with formal models of the causative cognitive mechanisms. The first coursebook to deal exclusively with the topic, it provides a main text for elective courses and a supplementary text for courses such as cognitive psychology and neuroscience.

Intelligent mental representations of physical, cognitive and social environments allow humans to navigate enormous search spaces, whose sizes vastly exceed the number of neurons in the human brain. This allows us to solve a wide range of problems, such as the Traveling Salesperson Problem, insight problems, as well as mathematics and physics problems. As an area of research, problem solving has steadily grown over time. Researchers in Artificial Intelligence have been formulating theories of problem solving for the last 70 years. Psychologists, on the other hand, have focused their efforts on documenting the observed behavior of subjects solving problems. This book represents the first effort to merge the behavioral results of human subjects with formal models of the causative cognitive mechanisms. The first coursebook to deal exclusively with the topic, it provides a main text for elective courses and a supplementary text for courses such as cognitive psychology and neuroscience.

This comprehensive and authoritative text/reference presents a unique, multidisciplinary perspective on Shape Perception in Human and Computer Vision. Rather than focusing purely on the state of the art, the book provides viewpoints from world-class researchers reflecting broadly on the issues that have shaped the field. Drawing upon many years of experience, each contributor discusses the trends followed and the progress made, in addition to identifying the major challenges that still lie ahead. Topics and features: examines each topic from a range of viewpoints, rather than promoting a specific paradigm; discusses topics on contours, shape hierarchies, shape grammars, shape priors, and 3D shape inference; reviews issues relating to surfaces, invariants, parts, multiple views, learning, simplicity, shape constancy and shape illusions; addresses concepts from the historically separate disciplines of computer vision and human vision using the same “language” and methods.

This comprehensive and authoritative text/reference presents a unique, multidisciplinary perspective on Shape Perception in Human and Computer Vision. Rather than focusing purely on the state of the art, the book provides viewpoints from world-class researchers reflecting broadly on the issues that have shaped the field. Drawing upon many years of experience, each contributor discusses the trends followed and the progress made, in addition to identifying the major challenges that still lie ahead. Topics and features: examines each topic from a range of viewpoints, rather than promoting a specific paradigm; discusses topics on contours, shape hierarchies, shape grammars, shape priors, and 3D shape inference; reviews issues relating to surfaces, invariants, parts, multiple views, learning, simplicity, shape constancy and shape illusions; addresses concepts from the historically separate disciplines of computer vision and human vision using the same “language” and methods.