Book contents
- Biological and Computer Vision
- Biological and Computer Vision
- Copyright page
- Dedication
- Contents
- Figures
- Preface
- Acknowledgments
- Abbreviations
- 1 Introduction to the World of Vision
- 2 The Travels of a Photon
- 3 The Phenomenology of Seeing
- 4 Creating and Altering Visual Percepts through Lesions and Electrical Stimulation
- 5 Adventures into Terra Incognita
- 6 From the Highest Echelons of Visual Processing to Cognition
- 7 Neurobiologically Plausible Computational Models
- 8 Teaching Computers How to See
- 9 Toward a World with Intelligent Machines That Can Interpret the Visual World
- 10 Visual Consciousness
- Index
- References
8 - Teaching Computers How to See
Published online by Cambridge University Press: 05 February 2021
Book contents
- Biological and Computer Vision
- Biological and Computer Vision
- Copyright page
- Dedication
- Contents
- Figures
- Preface
- Acknowledgments
- Abbreviations
- 1 Introduction to the World of Vision
- 2 The Travels of a Photon
- 3 The Phenomenology of Seeing
- 4 Creating and Altering Visual Percepts through Lesions and Electrical Stimulation
- 5 Adventures into Terra Incognita
- 6 From the Highest Echelons of Visual Processing to Cognition
- 7 Neurobiologically Plausible Computational Models
- 8 Teaching Computers How to See
- 9 Toward a World with Intelligent Machines That Can Interpret the Visual World
- 10 Visual Consciousness
- Index
- References
Summary
We have come a long way since our initial steps toward defining the basic properties of vision in Chapter 1. We started with characterizing the spatial and temporal statistics of natural images (Chapter 2). We summarized visual behavior – that is, how observers perceive the images around them (Chapter 3). Lesion studies helped define specific circuits in the cortex that are responsible for processing distinct types of visual information (Chapter 4). We explored how neurons in the retina, the thalamus, and the ventral visual cortex respond to a variety of different stimulus conditions (Chapters 2, 5, and 6).
- Type
- Chapter
- Information
- Biological and Computer Vision , pp. 152 - 191Publisher: Cambridge University PressPrint publication year: 2021
References
Further Reading
• Krizhevsky, A.; Sutskever, I.; and Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Presented at Neural Information Processing Systems, Montreal.Google Scholar
• Rao, R. P., and Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2:79–87.CrossRefGoogle ScholarPubMed
• Riesenhuber, M., and Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience 2:1019–1025.CrossRefGoogle ScholarPubMed
• Serre, T. (2019). Deep learning: the good, the bad and the ugly. Annual Review of Vision 5: 399–426.CrossRefGoogle ScholarPubMed
• Yamins, D. L.; Hong, H.; Cadieu, C.F.; Solomon, E. A.; Seibert, D., and DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences of the United States of America 111:8619–8624.CrossRefGoogle ScholarPubMed