Synthetic vision :: using volume learning and visual DNA /
In Synthetic Vision: Using Volume Learning and Visual DNA, a holistic model of the human visual system is developed into a working model in C++, informed by the latest neuroscience, DNN, and computer vision research. The author's synthetic visual pathway model includes the eye, LGN, visual cort...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston ; Berlin :
De/G Press,
[2018]
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| Online-Zugang: | Volltext |
| Zusammenfassung: | In Synthetic Vision: Using Volume Learning and Visual DNA, a holistic model of the human visual system is developed into a working model in C++, informed by the latest neuroscience, DNN, and computer vision research. The author's synthetic visual pathway model includes the eye, LGN, visual cortex, and the high level PFC learning centers. The corresponding visual genome model (VGM), begun in 2014, is introduced herein as the basis for a visual genome project analogous to the Human Genome Project funded by the US government. The VGM introduces volume learning principles and Visual DNA (VDNA) taking a multivariate approach beyond deep neural networks. Volume learning is modeled as programmable learning and reasoning agents, providing rich methods for structured agent classification networks. Volume learning incorporates a massive volume of multivariate features in various data space projections, collected into strands of Visual DNA, analogous to human DNA genes. VGM lays a foundation for a visual genome project to sequence VDNA as visual genomes in a public database, using collaborative research to move synthetic vision science forward and enable new applications. Bibliographical references are provided to key neuroscience, computer vision, and deep learning research, which form the basis for the biologically plausible VGM model and the synthetic visual pathway. The book also includes graphical illustrations and C++ API reference materials to enable VGM application programming. Open source code licenses are available for engineers and scientists. Scott Krig founded Krig Research to provide some of the world's first vision and imaging systems worldwide for military, industry, government, and academic use. Krig has worked for major corporations and startups in the areas of machine learning, computer vision, imaging, graphics, robotics and automation, computer security and cryptography. He has authored international patents in the areas of computer architecture, communications, computer security, digital imaging, and computer vision, and studied at Stanford. Scott Krig is the author of the English/Chinese Springer book Computer Vision Metrics, Survey, Taxonomy and Analysis of Computer Vision, Visual Neuroscience, and Deep Learning, Textbook Edition, as well as other books, articles, and papers. |
| Beschreibung: | 1 online resource (xviii, 350 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781501505966 1501505963 9781501506291 1501506293 |
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| 505 | 0 | 0 | |t Frontmatter -- |t About De/G PRESS -- |t Acknowledgments -- |t Contents -- |t Preface -- |t Chapter 1: Synthetic Vision Using Volume Learning and Visual DNA -- |t Chapter 2: Eye/LGN Model -- |t Chapter 3: Memory Model and Visual Cortex -- |t Chapter 4: Learning and Reasoning Agents -- |t Chapter 5: VGM Platform Overview -- |t Chapter 6: Volume Projection Metrics -- |t Chapter 7: Color 2D Region Metrics -- |t Chapter 8: Shape Metrics -- |t Chapter 9: Texture Metrics -- |t Chapter 10: Region Glyph Metrics -- |t Chapter 11: Applications, Training, Results -- |t Chapter 12: Visual Genome Project -- |t Bibliography -- |t Index. |
| 520 | |a In Synthetic Vision: Using Volume Learning and Visual DNA, a holistic model of the human visual system is developed into a working model in C++, informed by the latest neuroscience, DNN, and computer vision research. The author's synthetic visual pathway model includes the eye, LGN, visual cortex, and the high level PFC learning centers. The corresponding visual genome model (VGM), begun in 2014, is introduced herein as the basis for a visual genome project analogous to the Human Genome Project funded by the US government. The VGM introduces volume learning principles and Visual DNA (VDNA) taking a multivariate approach beyond deep neural networks. Volume learning is modeled as programmable learning and reasoning agents, providing rich methods for structured agent classification networks. Volume learning incorporates a massive volume of multivariate features in various data space projections, collected into strands of Visual DNA, analogous to human DNA genes. VGM lays a foundation for a visual genome project to sequence VDNA as visual genomes in a public database, using collaborative research to move synthetic vision science forward and enable new applications. Bibliographical references are provided to key neuroscience, computer vision, and deep learning research, which form the basis for the biologically plausible VGM model and the synthetic visual pathway. The book also includes graphical illustrations and C++ API reference materials to enable VGM application programming. Open source code licenses are available for engineers and scientists. Scott Krig founded Krig Research to provide some of the world's first vision and imaging systems worldwide for military, industry, government, and academic use. Krig has worked for major corporations and startups in the areas of machine learning, computer vision, imaging, graphics, robotics and automation, computer security and cryptography. He has authored international patents in the areas of computer architecture, communications, computer security, digital imaging, and computer vision, and studied at Stanford. Scott Krig is the author of the English/Chinese Springer book Computer Vision Metrics, Survey, Taxonomy and Analysis of Computer Vision, Visual Neuroscience, and Deep Learning, Textbook Edition, as well as other books, articles, and papers. | ||
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| contents | Frontmatter -- About De/G PRESS -- Acknowledgments -- Contents -- Preface -- Chapter 1: Synthetic Vision Using Volume Learning and Visual DNA -- Chapter 2: Eye/LGN Model -- Chapter 3: Memory Model and Visual Cortex -- Chapter 4: Learning and Reasoning Agents -- Chapter 5: VGM Platform Overview -- Chapter 6: Volume Projection Metrics -- Chapter 7: Color 2D Region Metrics -- Chapter 8: Shape Metrics -- Chapter 9: Texture Metrics -- Chapter 10: Region Glyph Metrics -- Chapter 11: Applications, Training, Results -- Chapter 12: Visual Genome Project -- Bibliography -- Index. |
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| spelling | Krig, Scott, author. http://id.loc.gov/authorities/names/no2015027897 Synthetic vision : using volume learning and visual DNA / Scott Krig. Boston ; Berlin : De/G Press, [2018] ©2018 1 online resource (xviii, 350 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rdaft Includes bibliographical references and index. Frontmatter -- About De/G PRESS -- Acknowledgments -- Contents -- Preface -- Chapter 1: Synthetic Vision Using Volume Learning and Visual DNA -- Chapter 2: Eye/LGN Model -- Chapter 3: Memory Model and Visual Cortex -- Chapter 4: Learning and Reasoning Agents -- Chapter 5: VGM Platform Overview -- Chapter 6: Volume Projection Metrics -- Chapter 7: Color 2D Region Metrics -- Chapter 8: Shape Metrics -- Chapter 9: Texture Metrics -- Chapter 10: Region Glyph Metrics -- Chapter 11: Applications, Training, Results -- Chapter 12: Visual Genome Project -- Bibliography -- Index. In Synthetic Vision: Using Volume Learning and Visual DNA, a holistic model of the human visual system is developed into a working model in C++, informed by the latest neuroscience, DNN, and computer vision research. The author's synthetic visual pathway model includes the eye, LGN, visual cortex, and the high level PFC learning centers. The corresponding visual genome model (VGM), begun in 2014, is introduced herein as the basis for a visual genome project analogous to the Human Genome Project funded by the US government. The VGM introduces volume learning principles and Visual DNA (VDNA) taking a multivariate approach beyond deep neural networks. Volume learning is modeled as programmable learning and reasoning agents, providing rich methods for structured agent classification networks. Volume learning incorporates a massive volume of multivariate features in various data space projections, collected into strands of Visual DNA, analogous to human DNA genes. VGM lays a foundation for a visual genome project to sequence VDNA as visual genomes in a public database, using collaborative research to move synthetic vision science forward and enable new applications. Bibliographical references are provided to key neuroscience, computer vision, and deep learning research, which form the basis for the biologically plausible VGM model and the synthetic visual pathway. The book also includes graphical illustrations and C++ API reference materials to enable VGM application programming. Open source code licenses are available for engineers and scientists. Scott Krig founded Krig Research to provide some of the world's first vision and imaging systems worldwide for military, industry, government, and academic use. Krig has worked for major corporations and startups in the areas of machine learning, computer vision, imaging, graphics, robotics and automation, computer security and cryptography. He has authored international patents in the areas of computer architecture, communications, computer security, digital imaging, and computer vision, and studied at Stanford. Scott Krig is the author of the English/Chinese Springer book Computer Vision Metrics, Survey, Taxonomy and Analysis of Computer Vision, Visual Neuroscience, and Deep Learning, Textbook Edition, as well as other books, articles, and papers. Online resource; title from PDF title page (publisher's Web site, viewed 21. Aug 2018). Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Vision par ordinateur. COMPUTERS General. bisacsh Computer vision fast Print version: Krig, Scott. Synthetic vision. Boston : Walter de Gruyter, [2018] 9781501515170 (DLC) 2018287155 (OCoLC)991799519 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1857417 Volltext |
| spellingShingle | Krig, Scott Synthetic vision : using volume learning and visual DNA / Frontmatter -- About De/G PRESS -- Acknowledgments -- Contents -- Preface -- Chapter 1: Synthetic Vision Using Volume Learning and Visual DNA -- Chapter 2: Eye/LGN Model -- Chapter 3: Memory Model and Visual Cortex -- Chapter 4: Learning and Reasoning Agents -- Chapter 5: VGM Platform Overview -- Chapter 6: Volume Projection Metrics -- Chapter 7: Color 2D Region Metrics -- Chapter 8: Shape Metrics -- Chapter 9: Texture Metrics -- Chapter 10: Region Glyph Metrics -- Chapter 11: Applications, Training, Results -- Chapter 12: Visual Genome Project -- Bibliography -- Index. Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Vision par ordinateur. COMPUTERS General. bisacsh Computer vision fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85029549 |
| title | Synthetic vision : using volume learning and visual DNA / |
| title_alt | Frontmatter -- About De/G PRESS -- Acknowledgments -- Contents -- Preface -- Chapter 1: Synthetic Vision Using Volume Learning and Visual DNA -- Chapter 2: Eye/LGN Model -- Chapter 3: Memory Model and Visual Cortex -- Chapter 4: Learning and Reasoning Agents -- Chapter 5: VGM Platform Overview -- Chapter 6: Volume Projection Metrics -- Chapter 7: Color 2D Region Metrics -- Chapter 8: Shape Metrics -- Chapter 9: Texture Metrics -- Chapter 10: Region Glyph Metrics -- Chapter 11: Applications, Training, Results -- Chapter 12: Visual Genome Project -- Bibliography -- Index. |
| title_auth | Synthetic vision : using volume learning and visual DNA / |
| title_exact_search | Synthetic vision : using volume learning and visual DNA / |
| title_full | Synthetic vision : using volume learning and visual DNA / Scott Krig. |
| title_fullStr | Synthetic vision : using volume learning and visual DNA / Scott Krig. |
| title_full_unstemmed | Synthetic vision : using volume learning and visual DNA / Scott Krig. |
| title_short | Synthetic vision : |
| title_sort | synthetic vision using volume learning and visual dna |
| title_sub | using volume learning and visual DNA / |
| topic | Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Vision par ordinateur. COMPUTERS General. bisacsh Computer vision fast |
| topic_facet | Computer vision. Vision par ordinateur. COMPUTERS General. Computer vision |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1857417 |
| work_keys_str_mv | AT krigscott syntheticvisionusingvolumelearningandvisualdna |