Verwandte Artikel zu Synthetic Vision: Using Volume Learning and Visual...
Reseña del editor
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. 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. 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.
Biografía del autor
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
- VerlagDe|G Press
- Erscheinungsdatum2018
- ISBN 10 1501515179
- ISBN 13 9781501515170
- EinbandTapa blanda
- Anzahl der Seiten368
Gebraucht kaufen
Zustand: Sehr gutZustand: Sehr gut - Gepflegter,... Mehr zu diesem Angebot erfahren
EUR 61,14
Versand:
EUR 45,00
Von Deutschland nach USA
Neu kaufen
Mehr zu diesem Angebot erfahren
EUR 109,05
Versand:
EUR 32,99
Von Deutschland nach USA
Beste Suchergebnisse beim ZVAB
Beispielbild für diese ISBN
Synthetic Vision : Using Volume Learning and Visual DNA
Anbieter: Buchpark, Trebbin, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. 1. Auflage. | Seiten: 368 | Sprache: Englisch | Produktart: Bücher. Artikel-Nr. 29103067/12
Anzahl: 1 verfügbar
Foto des Verkäufers
Synthetic Vision : Using Volume Learning and Visual DNA
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9781501515170
Anzahl: 2 verfügbar
Foto des Verkäufers
Synthetic Vision
Anbieter: moluna, Greven, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. 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. Artikel-Nr. 206513320
Anzahl: 1 verfügbar