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Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design
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Synthetic Vision - Using Volume Learning and Visual DNA (Paperback)
Loot Price: R3,473
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Synthetic Vision - Using Volume Learning and Visual DNA (Paperback)
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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.
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