Table of Contents
Cover
Related Titles
Title Page
Copyright
List of Contributors
Foreword
Part I: Fundamentals
Chapter 1: Introduction
1.1 Why Should We Be Inspired by Biology?
1.2 Organization of Chapters in the Book
1.3 Conclusion
Acknowledgments
References
Chapter 2: Bioinspired Vision Sensing
2.1 Introduction
2.2 Fundamentals and Motivation: Bioinspired Artificial Vision
2.3 From Biological Models to Practical Vision Devices
2.4 Conclusions and Outlook
References
Chapter 3: Retinal Processing: From Biology to Models and Applications
3.1 Introduction
3.2 Anatomy and Physiology of the Retina
3.3 Models of Vision
3.4 Application to Digital Photography
3.5 Conclusion
References
Chapter 4: Modeling Natural Image Statistics
4.1 Introduction
4.2 Why Model Natural Images?
4.3 Natural Image Models
4.4 Computer Vision Applications
4.5 Biological Adaptations to Natural Images
4.6 Conclusions
References
Chapter 5: Perceptual Psychophysics
5.1 Introduction
5.2 Laboratory Methods
5.3 Psychophysical Threshold Measurement
5.4 Classic Psychophysics: Theory and Methods
5.5 Signal Detection Theory
5.6 Psychophysical Scaling Methods
5.7 Conclusions
References
Part II: Sensing
Chapter 6: Bioinspired Optical Imaging
6.1 Visual Perception
6.2 Polarization Vision - Object Differentiation/Recognition
6.3 High-Speed Motion Detection
6.4 Conclusion
References
Chapter 7: Biomimetic Vision Systems
7.1 Introduction
7.2 Scaling Laws in Optics
7.3 The Evolution of Vision Systems
7.4 Manufacturing of Optics for Miniaturized Vision Systems
7.5 Examples for Biomimetic Compound Vision Systems
References
Chapter 8: Plenoptic Cameras
8.1 Introduction
8.2 Light Field Representation of the Plenoptic Function
8.3 The Plenoptic Camera
8.4 Applications of the Plenoptic Camera
8.5 Generalizations of the Plenoptic Camera
8.6 High-Performance Computing with Plenoptic Cameras
8.7 Conclusions
References
Part III: Modelling
Chapter 9: Probabilistic Inference and Bayesian Priors in Visual Perception
9.1 Introduction
9.2 Perception as Bayesian Inference
9.3 Perceptual Priors
9.4 Outstanding Questions
References
Chapter 10: From Neuronal Models to Neuronal Dynamics and Image Processing
10.1 Introduction
10.2 The Membrane Equation as a Neuron Model
10.3 Application 1: A Dynamical Retinal Model
10.4 Application 2: Texture Segregation
10.5 Application 3: Detection of Collision Threats
10.6 Conclusions
Acknowledgments
References
Chapter 11: Computational Models of Visual Attention and Applications
11.1 Introduction
11.2 Models of Visual Attention
11.3 A Closer Look at Cognitive Models
11.4 Applications
11.5 Conclusion
References
Chapter 12: Visual Motion Processing and Human Tracking Behavior
12.1 Introduction
12.2 Pursuit Initiation: Facing Uncertainties
12.3 Predicting Future and On-Going Target Motion
12.4 Dynamic Integration of Retinal and Extra-Retinal Motion Information: Computational Models
12.5 Reacting, Inferring, Predicting: A Neural Workspace
12.6 Conclusion
Acknowledgments
References
Chapter 13: Cortical Networks of Visual Recognition
13.1 Introduction
13.2 Global Organization of the Visual Cortex
13.3 Local Operations: Receptive Fields
13.4 Local Operations in V1
13.5 Multilayer Models
13.6 A Basic Introductory Model
13.7 Idealized Mathematical Model of V1: Fiber Bundle
13.8 Horizontal Connections and the Association Field
13.9 Feedback and Attentional Mechanisms
13.10 Temporal Considerations, Transformations and Invariance
13.11 Conclusion
References
Chapter 14: Sparse Models for Computer Vision
14.1 Motivation
14.2 What Is Sparseness? Application to Image Patches
14.3 SparseLets: A Multiscale, Sparse, Biologically Inspired Representation of Natural Images
14.4 SparseEdges: Introducing Prior Information
14.5 Conclusion
Acknowledgments
References
Chapter 15: Biologically Inspired Keypoints
15.1 Introduction
15.2 Definitions
15.3 What Does the Frond-End of the Visual System Tell Us?
15.4 Bioplausible Keypoint Extraction
15.5 Biologically Inspired Keypoint Representation
15.6 Qualitative Analysis: Visualizing Keypoint Information
15.7 Conclusions
References
Part IV: Applications
Chapter 16: Nightvision Based on a Biological Model
16.1 Introduction
16.2 Why Is Vision Difficult in Dim Light?
16.3 Why Is Digital Imaging Difficult in Dim Light?
16.4 Solving the Problem of Imaging in Dim Light
16.5 Implementation and Evaluation of the Night-Vision Algorithm
16.6 Conclusions
Acknowledgment
References
Chapter 17: Bioinspired Motion Detection Based on an FPGA Platform
17.1 Introduction
17.2 A Motion Detection Module for Robotics and Biology
17.3 Insect Motion Detection Models
17.4 Overview of Robotic Implementations of Bioinspired Motion Detection
17.5 An FPGA-Based Implementation
17.6 Experimental Results
17.7 Discussion
17.8 Conclusion
Acknowledgments
References
Chapter 18: Visual Navigation in a Cluttered World
18.1 Introduction
18.2 Cues from Optic Flow: Visually Guided Navigation
18.3 Estimation of Self-Motion: Knowing Where You Are Going
18.4 Object Detection: Understanding What Is in Your Way
18.5 Estimation of TTC: Time Constraints from the Expansion Rate
18.6 Steering Control: The Importance of Representation
18.7 Conclusions
Acknowledgments
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Foreword
Part I: Fundamentals
Begin Reading