Exploratory Vision: The Active Eye / Edition 1

Exploratory Vision: The Active Eye / Edition 1

by Michael S. Landy
     
 

ISBN-10: 0387945636

ISBN-13: 9780387945637

Pub. Date: 11/09/1995

Publisher: Springer New York

This book is a dialogue between researchers who study biological visual and computer scientists and engineers who seek to build computer vision systems that actively explore the environment. By describing new and important ways to design robots analogous to biological visual systems, it provides deep insights into the problems and solutions of computer vision. The…  See more details below

Overview

This book is a dialogue between researchers who study biological visual and computer scientists and engineers who seek to build computer vision systems that actively explore the environment. By describing new and important ways to design robots analogous to biological visual systems, it provides deep insights into the problems and solutions of computer vision. The book is divided into four parts, each addressing a different aspect of exploratory or active vision in biological and machine vision systems. The chapters are written by a cross-disciplinary selection of leading researchers who study computer and biological vision. As a result, many researchers and students concerned with vision will find this an invaluable survey to this fast-moving field.

Product Details

ISBN-13:
9780387945637
Publisher:
Springer New York
Publication date:
11/09/1995
Series:
Springer Series in Perception Engineering
Edition description:
1996
Pages:
362
Product dimensions:
6.14(w) x 9.21(h) x 0.03(d)

Table of Contents

I Active Human Vision.- 1 Moveo Ergo Video: Natural Retinal Image Motion and its Effect on Vision.- 1.1 Prologue.- 1.2 Introduction.- 1.3 Relation Between Eye Movement and Visual Acuity Circa 1900.- 1.4 The Marshall-Talbot Dynamic Theory of Visual Acuity.- 1.5 Empirical Tests of the Marshall-Talbot Theory.- 1.6 The Phone Rang.- 1.7 The Phone Rang Again.- 1.8 Retinal Image Slip Following Adaptation of the VOR.- 1.9 An Intramural Phone Call.- 1.10 References.- 2 Cogito Ergo Moveo: Cognitive Control of Eye Movement.- 2.1 Introduction.- 2.2 Example 1: Selection of the Target for Smooth Eye Movements.- 2.2.1 Smooth eye movements in the presence of visual backgrounds.- 2.2.2 The role of selective attention.- 2.3 Example 2: Predicting the Future Position of Targets.- 2.3.1 The effect of expectations on smooth eye movements.- 2.3.2 Past history vs. cognitive expectations of future target motion.- 2.4 Example 3: Planning Sequences of Saccades.- 2.5 Example 4: Saccades to Selected Targets in the Presence of Irrelevant Visual Backgrounds.- 2.6 Summary and Conclusions.- 2.7 References.- II Solving Visual Problems.- 3 Robust Computational Vision.- 3.1 Introduction.- 3.2 Vision Problems.- 3.3 Vision Methods.- 3.4 Robust Methods.- 3.5 Applications.- 3.5.1 Surface reconstruction.- 3.5.2 Image flow.- 3.5.3 Dynamic stereo.- 3.6 Discussion.- 3.6.1 Comparison with other paradigms.- 3.6.2 Improving performance.- 3.6.3 Computational resources.- 3.6.4 Further work.- 3.7 References.- 4 Eye Movements and the Complexity of Visual Processing.- 4.1 Introduction.- 4.2 Visual Task Performance.- 4.2.1 Detection.- 4.2.2 Masking.- 4.2.3 Localization.- 4.2.4 Multidimensional tasks.- 4.2.5 Speed-accuracy tradeoff.- 4.3 Task Complexity.- 4.3.1 Theory of complexity.- 4.3.2 Capacity of constrained parallel machines.- 4.3.3 Sequential machines.- 4.3.4 Theoretical speed-accuracy tradeoff.- 4.4 Translation Invariance.- 4.5 Conclusion.- 4.6 References.- 5 Exploratory Vision: Some Implications for Retinal Sampling and Reconstruction.- 5.1 Introduction.- 5.2 From Scene to Sensor to Code.- 5.3 Linear Reconstruction and the Sampling Theorem.- 5.4 Linear Reconstruction and Aliasing.- 5.5 Nonlinear Constraints on Possible Images.- 5.6 Irregular Sampling Arrays and Aliasing.- 5.7 Linear Reconstruction and Movement.- 5.8 Linear Reconstruction with Multiple Sampling Arrays.- 5.9 Ideal Arrays.- 5.10 Visual Representation and Transformational Constancy.- 5.11 Conclusion.- 5.12 References.- 6 Calibration of a Visual System with Receptor Drop-out.- 6.1 Introduction.- 6.1.1 Retinal degeneration and bisection judgments.- 6.1.2 Cone position calibration models.- 6.2 The Learning Algorithms.- 6.2.1 The visual system model.- 6.2.2 The delta rule.- 6.2.3 The TI rule.- 6.2.4 Inadequate sampling.- 6.2.5 A new rule.- 6.2.6 A final example.- 6.3 Discussion.- 6.3.1 Known translations.- 6.3.2 The interpolated image.- 6.3.3 Two views from two eyes.- 6.3.4 Partial damage.- 6.4 Conclusions.- 6.5 References.- 7 Peripheral Visual Field, Fixation and Direction of Heading.- 7.1 Introduction.- 7.2 Retinal Flow in a Rigid 2-D Universe.- 7.2.1 Calculating retinal flow.- 7.2.2 Level sets of retinal flow.- 7.3 Retinal Flow in a Rigid 3-D Universe.- 7.3.1 Calculating retinal flow.- 7.3.2 Points with zero flow in the 3-D universe.- 7.4 Latitudinal and Longitudinal Flow.- 7.4.1 Calculating latitudinal and longitudinal flow.- 7.4.2 Points with zero longitudinal flow in the 3-D universe.- 7.4.3 Points with zero latitudinal flow in the 3-D universe.- 7.5 A Systematic Pattern at the Periphery.- 7.6 Experiment I: Simulated Image Sequence.- 7.7 Experiment II: Servoing to a Target.- 7.8 Conclusion.- 7.9 References.- 8 Local Qualitative Shape from Active Shading.- 8.1 Introduction.- 8.2 Local Qualitative Shape.- 8.3 Diffuse Shading.- 8.3.1 A model of diffuse shading.- 8.3.2 An example.- 8.3.3 Diffuse shading in concavities.- 8.4 Point Source Shading.- 8.5 Active Shading.- 8.6 Conclusion.- 8.7 References.- III Robots that Explore.- 9 The Synthesis of Vision and Action.- 9.1 Prolegomena.- 9.2 Marr’s Theory and Its Drawbacks.- 9.3 The Architecture.- 9.3.1 The modules of the system.- 9.3.2 Outline of the approach.- 9.4 The Competences.- 9.4.1 Computational principles.- 9.4.2 Biological hierarchy.- 9.4.3 A hierarchy of models for navigational competences.- 9.4.4 Motion-based competences.- 9.4.5 A look at the motion pathway.- 9.4.6 Form-based competences.- 9.4.7 Spatial understanding.- 9.5 Conclusions.- 9.6 References.- 10 A Framework for Information Assimilation.- 10.1 Introduction.- 10.2 Information Assimilation: Formal Framework.- 10.2.1 Perceptual cycle.- 10.2.2 Sensor fusion and information assimilation.- 10.2.3 Environment Model.- 10.2.4 Input information tracks.- 10.2.5 Task modeling.- 10.2.6 Information assimilation.- 10.2.7 Knowledge caching for assimilation.- 10.3 Example Application: Autonomous Outdoor Navigation.- 10.3.1 Design.- 10.3.2 System architecture.- 10.3.3 Information assimilation module.- 10.4 Applications of Information Assimilation.- 10.5 Conclusion.- 10.6 References.- 11 Task-Oriented Vision.- 11.1 Introduction.- 11.2 Systems Description.- 11.2.1 Rock sampling system.- 11.2.2 Bin picking system.- 11.3 System Analysis.- 11.3.1 Rock-sampling system.- 11.3.2 Bin-picking system.- 11.4 Task-Oriented Approach.- 11.5 Conclusion.- 11.6 References.- IV Human and Machine: Telepresence and Virtual Reality.- 12 Active Vision and Virtual Reality.- 12.1 Introduction.- 12.1.1 Virtual reality and telepresence.- 12.1.2 Active vision.- 12.1.3 Active telepresence.- 12.2 Generating Views.- 12.2.1 Camera calibration.- 12.2.2 Digitization.- 12.2.3 Active estimation of surface depth.- 12.2.4 Registration.- 12.2.5 Warping.- 12.2.6 Integration of views.- 12.2.7 Distortions.- 12.3 Results.- 12.3.1 Simulations.- 12.3.2 Digitized Image Interpolation.- 12.4 Discussion.- 12.5 References.- 13 A Novel Environment for Situated Vision and Behavior.- 13.1 Introduction.- 13.2 The “Looking at People” Domain.- 13.3 Attention and Intention.- 13.4 Action Selection with Time-Varying Goals.- 13.5 Routines for Looking at People.- 13.5.1 Domain constraints.- 13.5.2 Figure-ground processing.- 13.5.3 Scene projection and calibration.- 13.5.4 Hand tracking.- 13.5.5 Gesture interpretation.- 13.6 An Example Implementation: ALIVE.- 13.7 Conclusion.- 13.8 References.- Author Index.

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