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Synthetic Vision

Using Volume Learning and Visual DNA

Scott Krig (Autor)

Buch | Softcover

368 Seiten

2018
De|G Press (Verlag)
978-1-5015-1517-0 (ISBN)

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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.

Scott Krig founded Krig Research in 1988, providing some of the world's first vision and imaging systems worldwide for military, industry, government, and academic use. Scott 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. Scott is the author of several books, articles and papers, and studied at Stanford.

Part One

Neuroscience

Deep Learning

Computer Vision

State-of-the-art Methods

Part Two

The NeuroMatrix Model of the Human Visual System

The Model in Detail

Part Three

Using the Neuro Matric Library for Synthetic Vision

Coding Solutions in C++

Appendixes

Bibliography

Resources Guide

Erscheinungsdatum	12.08.2018
Zusatzinfo	15 Tables, black and white; 40 Illustrations, color; color graphics
Verlagsort	Boston
Sprache	englisch
Maße	170 x 240 mm
Gewicht	692 g
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
	Mathematik / Informatik ► Informatik ► Netzwerke
	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Wirtschaft ► Betriebswirtschaft / Management ► Planung / Organisation
Schlagworte	Computer Vision, Synthetic Vision, Computer Imaging, Visualization, Sensor, Image Processing
ISBN-10	1-5015-1517-9 / 1501515179
ISBN-13	978-1-5015-1517-0 / 9781501515170
Zustand	Neuware