' 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 contraints 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.'Vom Verlag:
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.