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 symmetrical 3D shapes ; 5.3 How our new theory of shape perception, based on stereoacuity, accounts for old results ; 5.4 3D movies: what they are, what they want to be, and what it costs ; 5.5 Bayesian model of binocular shape perception ; 5.6 Why we could claim that our model is complete ; 6. Figure-Ground Organization, which Breaks Camouflage in Everyday Life, Permits the Veridical Recovery of a 3D Scene ; 6.1 Estimating the orientation of the ground-plane ; 6.2 How a coarse analysis of the positions and sizes of objects can be made ; 6.3 How a useful top-view representation was produced ; 6.4 Finding objects in the 2D image ; 6.5 Extracting relevant edges, grouping them and establishing symmetry correspondence ; 6.6 What can be done with a spatially-global map of a 3D scene? ; 7. What Made This Possible and What Comes Next? ; 7.1 Five Important conceptual contributions ; 7.2 Three of our technical contributions ; 7.3 Making our machine perceive and predict in dynamical environments ; 7.4 Solving the Figure-Ground Organization Problem with only a single 2D image ; 7.5 Recognizing individual objects by using a fast search of memory.
