You are previewing Developing and Applying Biologically-Inspired Vision Systems.
O'Reilly logo
Developing and Applying Biologically-Inspired Vision Systems

Book Description

When comparing machine vision systems to the visual systems of humans and animals, there is much to be learned in terms of object segmentation, lighting invariance, and recognition of object categories. Studying the biological systems and applying the findings to the structure of computational vision models and artificial vision systems aims to be an essential approach of advancing the field of machine vision.Developing and Applying Biologically-Inspired Vision Systems: Interdisciplinary Concepts provides interdisciplinary research which evaluates the performance of machine visual models and systems in comparison to biological systems. Blending the ideas of current scientific knowledge and biological vision, this collection of new ideas intends to inspire approaches and cross-disciplinary research to applications in machine vision. 

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  5. Foreword
    1. ABOUT THE FOREWORD AUTHOR
  6. Preface
  7. Acknowledgment
  8. Section 1: Visual Attention
    1. Chapter 1: Influence of Movement Expertise on Visual Perception of Objects, Events and Motor Action
      1. ABSTRACT
      2. INTRODUCTION
      3. COMPUTATIONAL MODELING OF VISUAL ATTENTION
      4. RESULTS
      5. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      6. ACKNOWLEDGMENT
    2. Chapter 2: Computational Approaches to Measurement of Visual Attention
      1. ABSTRACT
      2. INTRODUCTION
      3. COMPUTATIONAL METHODOLOGIES TO ANALYZE VISUAL DATA
      4. DISCUSSION
    3. Chapter 3: Task, Timing, and Representation in Visual Object Recognition
      1. ABSTRACT
      2. INTRODUCTION
      3. EXPERIMENTAL GUIDANCE
      4. SELECTIVE TUNING
      5. OBJECT RECOGNITION FRAMEWORK
      6. OBJECT RECOGNITION
      7. CONCLUSION
    4. Chapter 4: Attention in Stereo Vision
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. THE NEED FOR ATTENTION
      4. 3. ATTENTION AND DEPTH
      5. 4. A SUFFICIENT MODEL
      6. 5 .THE SELECTIVE TUNING (ST) MODEL
      7. 6. SHIFTING ATTENTION IN DEPTH
      8. 7. ATTENTION AND BINOCULAR RIVALRY
      9. 8. ATTENTIONAL STEREO CORRESPONDENCE REVISITED
      10. 9. GENERAL DISCUSSION
  9. Section 2: Binocular Vision
    1. Chapter 5: Local Constraints for the Perception of Binocular 3D Motion
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. PERCEPTUAL BIAS UNDER UNCERTAINTY
      5. 4. THE APERTURE PROBLEM IN 3D
      6. 6. GENERAL DISCUSSION
      7. 7. FUTURE RESEARCH DIRECTIONS
      8. 8. CONCLUSION
    2. Chapter 6: Modeling Binocular and Motion Transparency Processing by Local Center-Surround Interactions
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. A CORTICAL MODEL FOR BINOCULAR AND MOTION TRANSPARENCY PROCESSING
      5. SIMULATION RESULTS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
    3. Chapter 7: Early Perception-Action Cycles in Binocular Vision
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CORTICAL ARCHITECTURES FOR 3D ACTIVE MEASUREMENTS IN THE PERIPERSONAL SPACE
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
  10. Section 3: Visual Cortical Structures
    1. Chapter 8: The Roles of Endstopped and Curvature Tuned Computations in a Hierarchical Representation of 2D Shape
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. INCLUDING ENDSTOPPING IN THE OBJECT RECOGNITION PATHWAY
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    2. Chapter 9: A Measure of Localization of Brain Activity for the Motion Aperture Problem Using Electroencephalograms
      1. ABSTRACT
      2. INTRODUCTION
      3. METHODS
      4. RESULTS
      5. DISCUSSION
      6. CONCLUSION
    3. Chapter 10: Mathematical Foundations Modeled after Neo-Cortex for Discovery and Understanding of Structures in Data
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. DICTIONARY LEARNING WITH A BETA PROCESS
      4. 3. HIERARCHICAL GRAPHICAL MODELS OF VISUAL PROCESSING OF THE VENTRAL STREAM
      5. 4. CONCLUSION
  11. Section 4: Artificial Vision Systems
    1. Chapter 11: Visual Behavior Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics
      1. ABSTRACT
      2. INTRODUCTION
      3. POLARIZATION BASED VISUAL BEHAVIOR IN THE ANIMAL KINGDOM
      4. POLARIZATION-INSPIRED MACHINE VISION APPLICATIONS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    2. Chapter 12: Implementation and Evaluation of a Computational Model of Attention for Computer Vision
      1. ABSTRACT
      2. INTRODUCTION
      3. THEORIES AND MECHANISMS OF BIOLOGICAL VISUAL ATTENTION
      4. COMPUTATIONAL MODELS OF ATTENTION
      5. A COMPETITIVE YET HIERARCHICAL ATTENTION MODEL
      6. EVALUATION OF THE MODEL
      7. CONCLUSION
    3. Chapter 13: Implementation of Biologically Inspired Components in Embedded Vision Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. VISUAL ATTENTION AND SALIENCE
      4. COMPUTATIONAL VISUAL ATTENTION
      5. BOTTOM-UP VISUAL ATTENTION MODELS
      6. EVALUATIONS ON THE SALIENCE PREDICTION OF VISUAL ATTENTION MODELS
      7. HARDWARE IMPLEMENTATION OF VISUAL ATTENTION PROCESSING
      8. CONCLUSION
    4. Chapter 14: Replicating the Role of the Human Retina for a Cortical Visual Neuroprosthesis
      1. ABSTRACT
      2. INTRODUCTION
      3. VISUAL NEUROPROSTHETICS
      4. BIO-INSPIRED RETINAL PROCESSING
      5. ADDITIONAL TOOLS FOR BIO-INSPIRED VISUAL IMPLANTS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
  12. Compilation of References
  13. About the Contributors