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 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.