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ویرایش:
نویسندگان: Zygmunt Pizlo & Yunfeng Li & Tadamasa Sawada & Robert M. Steinman
سری:
ISBN (شابک) : 9780199922543, 0199922543
ناشر: Oxford University Press Inc
سال نشر: 2014
تعداد صفحات: 265
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 7 مگابایت
در صورت تبدیل فایل کتاب Making a Machine That Sees Like Us به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب ساخت دستگاهی که مانند ما دیده می شود نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
این متن توضیح میدهد که چرا و چگونه ادراکات بصری ما میتوانند نمایش دقیقی از جهان «آنجا» به ما ارائه دهند. در طول مسیر، داستان ماشینی (مدل محاسباتی) ساخته شده توسط نویسندگان را روایت می کند که مشکل محاسباتی دشوار دیدن روش انسان را حل می کند.
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.
Content: 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.