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Making a machine that sees like us

Author: Zygmunt Pizlo; Yunfeng Li; Tadamasa Sawad; Robert M Steinman
Publisher: Oxford : Oxford University Press, 2014.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This text explains why and how our visual perceptions can provide us with an accurate representation of the world 'out there.' 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.
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Zygmunt Pizlo; Yunfeng Li; Tadamasa Sawad; Robert M Steinman
ISBN: 9780190228385 0190228385 9780199922543 0199922543
OCLC Number: 888971501
Description: 1 online resource.
Contents: Machine generated contents note: --
Making a Machine That Sees Like Us --
1. How the Stage Was Set When We Began --
1.1 Introduction --
1.2 What is this book about? --
1.3 Analytical and Operational definitions of shape --
1.4 Shape constancy as a phenomenon (something you can observe) --
1.5 Complexity makes shape unique --
1.6 How would the world look if we are wrong? --
1.7 What had happened in the real world while we were away --
1.8 Perception viewed as an Inverse Problem --
1.9 How Bayesian inference can be used for modeling perception --
1.10 What it means to have a model of vision, and why we need to have one --
1.11 End of the beginning. --
2. How This All Got Started --
2.1 Controversy about shape constancy: 1980 --
1995 --
2.2 Events surrounding the 29th European Conference on Visual Perception (ECVP), St. Petersburg, Russia, August 20 --
25, 2006 where we first announced our paradigm shift --
2.3 The role of constraints in recovering the 3D shapes of polyhedral objects from line-drawings --
2.4 Events surrounding the 31st European Conference on Visual Perception (ECVP) Utrecht, NL, August 24 --
28, 2008, where we had our first big public confrontation --
2.5 Monocular 3D shape recovery of both synthetic and real objects --
3. Symmetry in Vision, Inside and Outside of the Laboratory --
3.1 Why and how approximate computations make visual analyses fast and perfect: the perception of slanted 2D mirror-symmetrical figures --
3.2 How human beings perceive 2D mirror-symmetry from perspective images --
3.3 Why 3D mirror-symmetry is more difficult than 2D symmetry --
3.4 Updating the Ideal Observer: how human beings perceive 3D mirror-symmetry from perspective images --
3.5 Important role of Generalized Cones in 3D shape perception: how human beings perceive 3D translational-symmetry from perspective images --
3.6 Michael Layton's contribution to symmetry in shape perception --
3.7 Leeuwenberg's attempt to develop a "Structural" explanation of Gestalt phenomena --
4. Using Symmetry Is Not Simple --
4.1 What is really going on? Examining the relationship between simplicity and likelihood --
4.2 Clearly, simplicity is better than likelihood --
excluding degenerate views does not eliminate spurious 3D symmetrical interpretations --
4.3 What goes with what? A new kind of Correspondence Problem --
4.4 Everything becomes easier once symmetry is viewed as self-similarity: the first working solution of the Symmetry Correspondence Problem --
5. A Second View Makes 3D Shape Perception Perfect --
5.1 What we know about binocular vision and how we came to know it --
5.2 How we worked out the binocular perception of symmet.
Responsibility: Zygmunt Pizlo, Yunfeng Li, Tadamasa Sawad, and Robert M. Steinman.

Abstract:

Making a Machine That Sees Like Us explains why and how our visual perceptions can provide us with an accurate representation of the world 'out there.' Along the way, it tells the story of a machine  Read more...

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'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 Read more...

 
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