Biologically Inspired Computer Vision

As the state-of-the-art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important. Sources of data that have to be routinely dealt with today's applications include video transmission, wireless communication, automatic fingerprint processing, massive databanks, non-weary and accurate automatic airport screening, robust night vision, just to name a few. Multidisciplinary inputs from other disciplines such as physics, computational neuroscience, cognitive science, mathematics, and biology will have a fundamental impact in the progress of imaging and vision sciences. One of the advantages of the study of biological organisms is to devise very different type of computational paradigms by implementing a neural network with a high degree of local connectivity. This is a comprehensive and rigorous reference in the area of biologically motivated vision sensors. The study of biologically visual systems can be considered as a two way avenue. On the one hand, biological organisms can provide a source of inspiration for new computational efficient and robust vision models and on the other hand machine vision approaches can provide new insights for understanding biological visual systems. Along the different chapters, this book covers a wide range of topics from fundamental to more specialized topics, including visual analysis based on a computational level, hardware implementation, and the design of new more advanced vision sensors. The last two sections of the book provide an overview of a few representative applications and current state of the art of the research in this area. This makes it a valuable book for graduate, Master, PhD students and also researchers in the field.

Autorentext
Prof. Gabriel Cristobal is currently a Research Scientist at the Instituto de Optica, Spanish Council for Scientific Research (CSIC). His current research interests are joint representations, vision modelling and multidimensional signal processing. Prof. Cristobal is a Senior Member of the IEEE, member of the Optical Society of America (OSA), EURASIP Spanish liaison officer for the period 2009-2010 and member of the ISO/IEC JTC1/SC29/WG1 (JPEG2000). He is co-editor of the book "Optical and Digital Image Processing" by G. Cristobal, P. Schelkens and H. Thienpont, Wiley VCH, 2011.

Prof. Matthias Keil is currently Ramon and Cajal researcher in the Basic Psychology Department of the University of Barcelona (Spain). He received his PhD degree from the University of Ulm (Germany) for proposing a novel architecture for early visual information processing in the human brain. His research interests are centered on computational neuroscience and diffusion-based image processing. Examples of former and current research lines include computational modeling of brightness and lightness perception, tone mapping, time to contact perception, modeling of insect vision, and biologically motivated collision avoidance systems.

Dr. Laurent Perrinet is researcher in Computational Neuroscience at the "Institut de Neurosciences de la Timone" at Aix-Marseille Universite, France. His research is focused on bridging the complex dynamics of realistic models of large-scale models of spiking neurons with functional models of low-level vision.

Klappentext
This book serves as a comprehensive but rigorous reference in the area of biologically inspired computer vision modeling. Biologically inspired vision, that is the study of visual systems of living beings, can be considered as a two-way process. On the one hand, living organisms can provide a source of inspiration for
new computationally efficient and robust vision models, and on the other hand, machine vision approaches can provide new insights into understanding biological visual systems. Over the different chapters, this book covers a wide range from the fundamental to the more specialized topics. It also analyzes the influence of these studies in the design of novel, more advanced vision sensors. In particular, the last section of the book provides an overview of a few representative applications and current state of the art of the research in this area. This book contains 18 chapters that have been organized in four different parts: Fundamentals Sensing Modeling Applications.

Inhalt

List of Contributors XV

Foreword XIX

Part I Fundamentals 1

1 Introduction 3
Gabriel Cristóbal, Laurent U. Perrinet, and Matthias S. Keil

1.1 Why ShouldWe Be Inspired by Biology? 4

1.2 Organization of Chapters in the Book 6

1.3 Conclusion 9

Acknowledgments 9

References 9

2 Bioinspired Vision Sensing 11
Christoph Posch

2.1 Introduction 11

2.1.1 Neuromorphic Engineering 12

2.1.2 Implementing Neuromorphic Systems 13

2.2 Fundamentals and Motivation: Bioinspired Artificial Vision 13

2.2.1 Limitations in Vision Engineering 14

2.2.2 The Human Retina from an Engineering Viewpoint 14

2.2.3 Modeling the Retina in Silicon 17

2.3 From Biological Models to Practical Vision Devices 18

2.3.1 TheWiring Problem 18

2.3.2 Where and What 20

2.3.3 Temporal Contrast: The DVS 21

2.3.4 Event-Driven Time-Domain Imaging: The ATIS 22

2.4 Conclusions and Outlook 25

References 26

3 Retinal Processing: From Biology to Models and Applications 29
David Alleysson and Nathalie Guyader

3.1 Introduction 29

3.2 Anatomy and Physiology of the Retina 30

3.2.1 Overview of the Retina 30

3.2.2 Photoreceptors 31

3.2.3 Outer and Inner Plexiform Layers (OPL and IPL) 33

3.2.4 Summary 34

3.3 Models of Vision 34

3.3.1 Overview of the Retina Models 34

3.3.2 Biological Models 35

3.3.2.1 Cellular and Molecular Models 35

3.3.2.2 Network Models 36

3.3.2.3 Parallel and Descriptive Models 38

3.3.3 Information Models 39

3.3.4 Geometry Models 40

3.4 Application to Digital Photography 42

3.4.1 Color Demosaicing 43

3.4.2 Color Constancy Chromatic Adaptation White Balance Tone Mapping 44

3.5 Conclusion 45

References 46

4 Modeling Natural Image Statistics 53
Holly E. Gerhard, Lucas Theis, and Matthias Bethge

4.1 Introduction 53

4.2 Why Model Natural Images? 53

4.3 Natural Image Models 55

4.3.1 Model Evaluation 65

4.4 Computer Vision Applications 69

4.5 Biological Adaptations to Natural Images 71

4.6 Conclusions 75

References 76

5 Perceptual Psychophysics 81
C. Alejandro Parraga

5.1 Introduction 81

5.1.1 What Is Psychophysics andWhy DoWe Need It? 81

5.2 Laboratory Methods 82

5.2.1 Accuracy and Precision 83

5.2.2 Error Propagation 84

5.3 PsychophysicalThreshold Measurement 85

5.3.1 Weber's Law 85

5.3.2 Sensitivity Functions 86

5.4 Classic Psychophysics:Theory and Methods 86

5.4.1 Theory 87

5.4.2 Method of Constant Stimuli 88

5.4.3 Method of Limits 90

5.4.3.1 Forced-Choice Methods 92

5.4.4 Method of Adjustments 93

5.4.5 Estimating Psychometric Function Parameters 94

5.5 Signal Detection Theory 94

5.5.1 Signal and Noise 94

5.5.2 The Receiver Operating Characteristic 96

5.6 Psychophysical Scaling Methods 98

5.6.1 Discrimination Scales 99

5.6.2 Rating Scales 100

5.6.2.1 Equipartition Scales 100

5.6.2.2 Paired Comparison Scales 101

5.7 Conclusions 105

References 106

Part II Sensing 109

6 Bioinspired Optical Imaging 111
Mukul Sarkar

6.1 Visual Perception 111

6.1.1 Natural Single-Aperture and Multiple-Aperture Eyes 112

6.1.1.1 Human Eyes 113

6.1.1.2 Compound Eyes 114

6.1.1.3 Resolution 114

6.1.1.4 Visual Acuity 115 <p&g...